Patent Grant
11774944
The present disclosure relates to methods and systems for data collection in industrial environments, as well as methods and systems for leveraging collected data for monitoring, remote control, autonomous action, and other activities in industrial environments.
Heavy industrial environments, such as environments for large scale manufacturing (such as of aircraft, ships, trucks, automobiles, and large industrial machines), energy production environments (such as oil and gas plants, renewable energy environments, and others), energy extraction environments (such as mining, drilling, and the like), construction environments (such as for construction of large buildings), and others, involve highly complex machines, devices and systems and highly complex workflows, in which operators must account for a host of parameters, metrics, and the like in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Historically, data has been collected in heavy industrial environments by human beings using dedicated data collectors, often recording batches of specific sensor data on media, such as tape or a hard drive, for later analysis. Batches of data have historically been returned to a central office for analysis, such as by undertaking signal processing or other analysis on the data collected by various sensors, after which analysis can be used as a basis for diagnosing problems in an environment and/or suggesting ways to improve operations. This work has historically taken place on a time scale of weeks or months, and has been directed to limited data sets.
The emergence of the Internet of Things (IoT) has made it possible to connect continuously to and among a much wider range of devices. Most such devices are consumer devices, such as lights, thermostats, and the like. More complex industrial environments remain more difficult, as the range of available data is often limited, and the complexity of dealing with data from multiple sensors makes it much more difficult to produce “smart” solutions that are effective for the industrial sector. A need exists for improved methods and systems for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments.
Methods and systems are provided herein for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, and intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments. These methods and systems include methods, systems, components, devices, workflows, services, processes, and the like that are deployed in various configurations and locations, such as: (a) at the “edge” of the Internet of Things, such as in the local environment of a heavy industrial machine; (b) in data transport networks that move data between local environments of heavy industrial machines and other environments, such as of other machines or of remote controllers, such as enterprises that own or operate the machines or the facilities in which the machines are operated; and (c) in locations where facilities are deployed to control machines or their environments, such as cloud-computing environments and on-premises computing environments of enterprises that own or control heavy industrial environments or the machines, devices or systems deployed in them. These methods and systems include a range of ways for providing improved data include a range of methods and systems for providing improved data collection, as well as methods and systems for deploying increased intelligence at the edge, in the network, and in the cloud or premises of the controller of an industrial environment.
Methods and systems are disclosed herein for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility.
Methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors.
Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system.
Methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial IoT devices, including on-device sensor fusion and data storage for an Industrial IoT device, where data from multiple sensors is multiplexed at the device for storage of a fused data stream.
Methods and systems are disclosed herein for a self-organizing data marketplace for industrial IoT data, including a self-organizing data marketplace for industrial IoT data, where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success.
Methods and systems are disclosed herein for self-organizing data pools, including self-organization of data pools based on utilization and/or yield metrics, including utilization and/or yield metrics that are tracked for a plurality of data pools.
Methods and systems are disclosed herein for training artificial intelligence (“AI”) models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, where the AI model operates on sensor data from an industrial environment.
Methods and systems are disclosed herein for a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm.
Methods and systems are disclosed herein for an industrial IoT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial IoT data.
Methods and systems are disclosed herein for a self-organizing collector, including a self-organizing, multi-sensor data collector that can optimize data collection, power and/or yield based on conditions in its environment.
Methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing and/or other network conditions.
Methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment.
Methods and systems are disclosed herein for a self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data.
Methods and systems are disclosed herein for a self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment.
Methods and systems are disclosed herein for a haptic or multi-sensory user interface, including a wearable haptic or multi-sensory user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs.
Methods and systems are disclosed herein for a presentation layer for augmented reality and virtual reality (AR/VR) industrial glasses, where heat map elements are presented based on patterns and/or parameters in collected data.
Methods and systems are disclosed herein for condition-sensitive, self-organized tuning of AR/VR interfaces based on feedback metrics and/or training in industrial environments.
In embodiments, a system for data collection, processing, and utilization of signals from at least a first element in a first machine in an industrial environment includes a platform including a computing environment connected to a local data collection system having at least a first sensor signal and a second sensor signal obtained from at least the first machine in the industrial environment. The system includes a first sensor in the local data collection system configured to be connected to the first machine and a second sensor in the local data collection system. The system further includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. The multiple outputs include a first output and second output configured to be switchable between a condition in which the first output is configured to switch between delivery of the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. Unassigned outputs are configured to be switched off producing a high-impedance state.
In embodiments, the first sensor signal and the second sensor signal are continuous vibration data about the industrial environment. In embodiments, the second sensor in the local data collection system is configured to be connected to the first machine. In embodiments, the second sensor in the local data collection system is configured to be connected to a second machine in the industrial environment. In embodiments, the computing environment of the platform is configured to compare relative phases of the first and second sensor signals. In embodiments, the first sensor is a single-axis sensor and the second sensor is a three-axis sensor. In embodiments, at least one of the multiple inputs of the crosspoint switch includes internet protocol, front-end signal conditioning, for improved signal-to-noise ratio. In embodiments, the crosspoint switch includes a third input that is configured with a continuously monitored alarm having a pre-determined trigger condition when the third input is unassigned to any of the multiple outputs.
In embodiments, the local data collection system includes multiple multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment. In embodiments, the local data collection system includes distributed complex programmable hardware device (“CPLD”) chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment. In embodiments, the local data collection system is configured to provide high-amperage input capability using solid state relays. In embodiments, the local data collection system is configured to power-down at least one of an analog sensor channel and a component board.
In embodiments, the distributed CPLD chips each dedicated to the data bus for logic control of the multiple multiplexing units and the multiple data acquisition units includes as high-frequency crystal clock reference configured to be divided by at least one of the distributed CPLD chips for at least one delta-sigma analog-to-digital converter to achieve lower sampling rates without digital resampling.
In embodiments, the local data collection system is configured to obtain long blocks of data at a single relatively high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, the single relatively high-sampling rate corresponds to a maximum frequency of about forty kilohertz. In embodiments, the long blocks of data are for a duration that is in excess of one minute. In embodiments, the local data collection system includes multiple data acquisition units each having an onboard card set configured to store calibration information and maintenance history of a data acquisition unit in which the onboard card set is located. In embodiments, the local data collection system is configured to plan data acquisition routes based on hierarchical templates.
In embodiments, the local data collection system is configured to manage data collection bands. In embodiments, the data collection bands define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. In embodiments, the local data collection system includes a neural net expert system using intelligent management of the data collection bands. In embodiments, the local data collection system is configured to create data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes. In embodiments, at least one of the hierarchical templates is associated with multiple interconnected elements of the first machine. In embodiments, at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine.
In embodiments, the local data collection system includes a graphical user interface (“GUI”) system configured to manage the data collection bands. In embodiments, the GUI system includes an expert system diagnostic tool. In embodiments, the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment. In embodiments, the platform is configured to provide self-organization of data pools based on at least one of the utilization metrics and yield metrics. In embodiments, the platform includes a self-organized swarm of industrial data collectors. In embodiments, the local data collection system includes a wearable haptic user interface for an industrial sensor data collector with at least one of vibration, heat, electrical, and sound outputs.
In embodiments, multiple inputs of the crosspoint switch include a third input connected to the second sensor and a fourth input connected to the second sensor. The first sensor signal is from a single-axis sensor at an unchanging location associated with the first machine. In embodiments, the second sensor is a three-axis sensor. In embodiments, the local data collection system is configured to record gap-free digital waveform data simultaneously from at least the first input, the second input, the third input, and the fourth input. In embodiments, the platform is configured to determine a change in relative phase based on the simultaneously recorded gap-free digital waveform data. In embodiments, the second sensor is configured to be movable to a plurality of positions associated with the first machine while obtaining the simultaneously recorded gap-free digital waveform data. In embodiments, multiple outputs of the crosspoint switch include a third output and fourth output. The second, third, and fourth outputs are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the platform is configured to determine an operating deflection shape based on the change in relative phase and the simultaneously recorded gap-free digital waveform data.
In embodiments, the unchanging location is a position associated with the rotating shaft of the first machine. In embodiments, tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions on the first machine but are each associated with different bearings in the machine. In embodiments, tri-axial sensors in the sequence of the tri-axial sensors are each located at similar positions associated with similar bearings but are each associated with different machines. In embodiments, the local data collection system is configured to obtain the simultaneously recorded gap-free digital waveform data from the first machine while the first machine and a second machine are both in operation. In embodiments, the local data collection system is configured to characterize a contribution from the first machine and the second machine in the simultaneously recorded gap-free digital waveform data from the first machine. In embodiments, the simultaneously recorded gap-free digital waveform data has a duration that is in excess of one minute.
In embodiments, a method of monitoring a machine having at least one shaft supported by a set of bearings includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method includes monitoring second, third, and fourth data channels each assigned to an axis of a three-axis sensor. The method includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation and determining a change in relative phase based on the digital waveform data.
In embodiments, the tri-axial sensor is located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors simultaneously. In embodiments, the method includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location is a position associated with the shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with the shaft of the machine. The tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine.
In embodiments, the method includes monitoring the first data channel assigned to the single-axis sensor at an unchanging location located on a second machine. The method includes monitoring the second, the third, and the fourth data channels, each assigned to the axis of a three-axis sensor that is located at the position associated with the second machine. The method also includes recording gap-free digital waveform data simultaneously from all of the data channels from the second machine while both of the machines are in operation. In embodiments, the method includes characterizing the contribution from each of the machines in the gap-free digital waveform data simultaneously from the second machine.
In embodiments, a method for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment includes obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine. The method includes connecting a first input of a crosspoint switch of the local data collection system to a first sensor and a second input of the crosspoint switch to a second sensor in the local data collection system. The method includes switching between a condition in which a first output of the crosspoint switch alternates between delivery of at least the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from a second output of the crosspoint switch. The method also includes switching off unassigned outputs of the crosspoint switch into a high-impedance state.
In embodiments, the first sensor signal and the second sensor signal are continuous vibration data from the industrial environment. In embodiments, the second sensor in the local data collection system is connected to the first machine. In embodiments, the second sensor in the local data collection system is connected to a second machine in the industrial environment. In embodiments, the method includes comparing, automatically with the computing environment, relative phases of the first and second sensor signals. In embodiments, the first sensor is a single-axis sensor and the second sensor is a three-axis sensor. In embodiments, at least the first input of the crosspoint switch includes internet protocol front-end signal conditioning for improved signal-to-noise ratio.
In embodiments, the method includes continuously monitoring at least a third input of the crosspoint switch with an alarm having a pre-determined trigger condition when the third input is unassigned to any of multiple outputs on the crosspoint switch. In embodiments, the local data collection system includes multiple multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment. In embodiments, the local data collection system includes distributed CPLD chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment. In embodiments, the local data collection system provides high-amperage input capability using solid state relays.
In embodiments, the method includes powering down at least one of an analog sensor channel and a component board of the local data collection system. In embodiments, the local data collection system includes an external voltage reference for an A/D zero reference that is independent of the voltage of the first sensor and the second sensor. In embodiments, the local data collection system includes a phase-lock loop band-pass tracking filter that obtain slow-speed RPMs and phase information. In embodiments, the method includes digitally deriving phase using on-board timers relative to at least one trigger channel and at least one of multiple inputs on the crosspoint switch.
In embodiments, the method includes auto-scaling with a peak-detector using a separate analog-to-digital converter for peak detection. In embodiments, the method includes routing at least one trigger channel that is one of raw and buffered into at least one of multiple inputs on the crosspoint switch. In embodiments, the method includes increasing input oversampling rates with at least one delta-sigma analog-to-digital converter to reduce sampling rate outputs and to minimize anti-aliasing filter requirements. In embodiments, the distributed CPLD chips are each dedicated to the data bus for logic control of the multiple multiplexing units and the multiple data acquisition units and each include a high-frequency crystal clock reference divided by at least one of the distributed CPLD chips for at least one delta-sigma analog-to-digital converter to achieve lower sampling rates without digital resampling. In embodiments, the method includes obtaining long blocks of data at a single relatively high-sampling rate with the local data collection system as opposed to multiple sets of data taken at different sampling rates. In embodiments, the single relatively high-sampling rate corresponds to a maximum frequency of about forty kilohertz. In embodiments, the long blocks of data are for a duration that is in excess of one minute. In embodiments, the local data collection system includes multiple data acquisition units and each data acquisition unit has an onboard card set that stores calibration information and maintenance history of a data acquisition unit in which the onboard card set is located.
In embodiments, the method includes planning data acquisition routes based on hierarchical templates associated with at least the first element in the first machine in the industrial environment. In embodiments, the local data collection system manages data collection bands that define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. In embodiments, the local data collection system includes a neural net expert system using intelligent management of the data collection bands. In embodiments, the local data collection system creates data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes. In embodiments, at least one of the hierarchical templates is associated with multiple interconnected elements of the first machine. In embodiments, at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine.
In embodiments, the method includes controlling a GUI system of the local data collection system to manage the data collection bands. The GUI system includes an expert system diagnostic tool. In embodiments, the computing environment of the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment. In embodiments, the computing environment of the platform provides self-organization of data pools based on at least one of the utilization metrics and yield metrics. In embodiments, the computing environment of the platform includes a self-organized swarm of industrial data collectors. In embodiments, each of multiple inputs of the crosspoint switch is individually assignable to any of multiple outputs of the crosspoint switch.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for capturing a plurality of streams of sensed data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine; at least one of the streams containing a plurality of frequencies of data. The method may include identifying a subset of data in at least one of the plurality of streams that corresponds to data representing at least one predefined frequency. The at least one predefined frequency is represented by a set of data collected from alternate sensors deployed to monitor aspects of the industrial machine associated with the at least one moving part of the machine. The method may further include processing the identified data with a data processing facility that processes the identified data with an algorithm configured to be applied to the set of data collected from alternate sensors. Lastly, the method may include storing the at least one of the streams of data, the identified subset of data, and a result of processing the identified data in an electronic data set.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing, and storage systems and may include a method for applying data captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. The data is captured with predefined lines of resolution covering a predefined frequency range and is sent to a frequency matching facility that identifies a subset of data streamed from other sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine. The streamed data includes a plurality of lines of resolution and frequency ranges. The subset of data identified corresponds to the lines of resolution and predefined frequency range. This method may include storing the subset of data in an electronic data record in a format that corresponds to a format of the data captured with predefined lines of resolution; and signaling to a data processing facility the presence of the stored subset of data. This method may, optionally, include processing the subset of data with at least one set of algorithms, models and pattern recognizers that corresponds to algorithms, models and pattern recognizers associated with processing the data captured with predefined lines of resolution covering a predefined frequency range.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for identifying a subset of streamed sensor data, the sensor data captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the subset of streamed sensor data at predefined lines of resolution for a predefined frequency range, and establishing a first logical route for communicating electronically between a first computing facility performing the identifying and a second computing facility, wherein identified subset of the streamed sensor data is communicated exclusively over the established first logical route when communicating the subset of streamed sensor data from the first facility to the second facility. This method may further include establishing a second logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that is not the identified subset. Additionally, this method may further include establishing a third logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that includes the identified subset and at least one other portion of the data not represented by the identified subset.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a first data sensing and processing system that captures first data from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the first data covering a set of lines of resolution and a frequency range. This system may include a second data sensing and processing system that captures and streams a second set of data from a second set of sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine, the second data covering a plurality of lines of resolution that includes the set of lines of resolution and a plurality of frequencies that includes the frequency range. The system may enable selecting a portion of the second data that corresponds to the set of lines of resolution and the frequency range of the first data, and processing the selected portion of the second data with the first data sensing and processing system.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for automatically processing a portion of a stream of sensed data. The sensed data is received from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. The sensed data is in response to an electronic data structure that facilitates extracting a subset of the stream of sensed data that corresponds to a set of sensed data received from a second set of sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The set of sensed data is constrained to a frequency range. The stream of sensed data includes a range of frequencies that exceeds the frequency range of the set of sensed data, the processing comprising executing an algorithm on a portion of the stream of sensed data that is constrained to the frequency range of the set of sensed data, the algorithm configured to process the set of sensed data.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for receiving first data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. This method may further include detecting at least one of a frequency range and lines of resolution represented by the first data; receiving a stream of data from sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The stream of data includes: (1) a plurality of frequency ranges and a plurality of lines of resolution that exceeds the frequency range and the lines of resolution represented by the first data; (2) a set of data extracted from the stream of data that corresponds to at least one of the frequency range and the lines of resolution represented by the first data; and (3) the extracted set of data which is processed with a data processing algorithm that is configured to process data within the frequency range and within the lines of resolution of the first data.
An example data collection system in an industrial environment includes a data collector communicatively coupled to a number of input channels for acquiring collected data, where the collected data is industrial internet-of-things data; a data storage structured to store the collected data that corresponds to the number of input channels as a number of data pools; and a self-organizing data marketplace engine that receives the number of data pools and is organized based on training a marketplace self-organization with a training set and based on feedback from measures of marketplace success with respect to the number of data pools.
Certain further aspects of an example system are described following, any one or more of which may be present in certain embodiments. An example system includes where the self-organizing data marketplace engine learns to improve the measures of marketplace success based on determining user favored combinations of data pools through a selected collection of routines; where the self-organizing data marketplace engine is an expert system utilizing a neural network to classify the collected data for marketplace analysis; where the number of data pools include a data storage profile with a storage time definition for the collected data; where the self-organizing data marketplace engine utilizes a self-organizing map that creates a topology for the stored collected data; where the data storage includes stored local data acquisition calibration information; where the data storage includes stored local data acquisition maintenance information; and/or where the data collector is one of a number of self-organized data collectors, where the number of self-organized data collectors organize among themselves to optimize data collection based at least in part on a received data marketplace indicator.
An example system for monitoring a power roller of a conveyor in an industrial environment includes a number of sensors disposed to sense conditions of the power roller, where each sensor of the number of sensors produces a corresponding analog signal representative of a sensed condition; an analog crosspoint switch including a number of inputs and a number of outputs, where the analog signals produced by the number of sensors connect to a portion of the number of inputs; and where the analog crosspoint switch is configurable to route a portion of the analog signals representing sensed conditions of the power roller to a number of the outputs. An example system further includes where the conditions of the power roller that are sensed by the number of sensors includes at least one of: a rate of rotation of the power roller, a load being transported by the power roller, a power amount consumed by the power roller, and/or a rate of acceleration of the power roller.
An example system for monitoring a fan in a factory setting includes a number of sensors disposed to sense conditions of the fan in the factory setting, where each sensor of the number of sensors produces a corresponding analog signal representative of a sensed condition; and an analog crosspoint switch including a number of inputs and a number of outputs, where the analog signals produced by the number of sensors connect to a portion of the number of outputs; and where the analog crosspoint switch is configurable to route a portion of the analog signals representing sensed conditions of the fan to a number of the outputs. An example system further includes where the sensed conditions of the fan in the factory setting by the number by the number of sensors include at least one of: a fan blade tip speed, a torque, a back pressure, a number of revolutions per minute, and/or a volume of air per unit time produced by the fan.
An example system for monitoring a turbine in a power generation environment includes a number of sensors disposed to sense conditions of the turbine, where each sensor of the number of sensors produces a corresponding analog signal representative of a sensed condition; an analog crosspoint switch including a number of inputs and a number of outputs, where the analog signals produced by the number of sensors connect to a portion of the number of inputs; and where the analog crosspoint switch is configurable to route a portion of the analog signals representing sensed conditions of the turbine to a number of the outputs. An example system further includes where the sensed conditions include at least one of: a relative shaft vibration, an absolute vibration of bearings, a turbine cover vibration, a thrust bearing axial vibration, a stator core vibration, a stator bar vibration, and/or a stator end winding vibration.
An example system for data collection in an industrial environment includes: a number of industrial condition sensing and acquisition modules; a number of programmable logic components, with at least one programmable logic component disposed on a corresponding one of each of the number of modules and controlling a portion of the sensing and acquisition functionality of the module on which it is disposed; and a communication bus for interconnecting each programmable logic component of the number of programmable logic components with other programmable logic component that are associated with different ones of the sensing and acquisition modules.
Certain further aspects of an example system are described following, any one or more of which are present in certain embodiments. An example system includes: where at least one programmable logic component is programmed via the communication bus; where the communication bus includes a portion that is dedicated to programming the programmable logic components; and/or where controlling a portion of the sensing and acquisition functionality of a module includes at least one power control function such as: controlling power of a sensor, controlling power of a multiplexer, controlling power of a portion of the module, and/or controlling a sleep mode of the programmable logic component. An example system includes: where controlling a portion of the sensing and acquisition functionality of a module includes providing a voltage reference to at least one of a sensor and an analog to digital converter disposed on the module; where controlling a portion of the sensing and acquisition functionality of a module includes detecting a relative phase of at least two analog signals derived from at least two corresponding sensors disposed on the module; where controlling a portion of the sensing and acquisition functionality of a module includes controlling a sampling of data provided by at least one sensor disposed on the module; where controlling a portion of the sensing and acquisition functionality of a module includes detecting a peak voltage of a signal provided by a sensor disposed on the module; and/or where controlling a portion of the sensing and acquisition functionality of a module includes configuring at least one multiplexer disposed on the module by specifying to the multiplexer a mapping of at least one input and one output.
An example system for data collection in an industrial environment includes a data collection system that monitors at least one signal for a set of collection band parameters (e.g., frequency bands) and, upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors based on the detected parameter. Example and non-limiting aspects of a system, any one or more of which may be present in certain embodiments, include: where the at least one signal includes an output of a sensor that senses a condition in the industrial environment; where the set of collection band parameters includes values derivable from the at least one signal that are beyond an acceptable range of values; where configuring portions of the system includes configuring a storage facility to accept data collected from the set of sensors; where configuring portions of the system includes configuring a data routing portion including at least one of an analog crosspoint switch, a hierarchical multiplexer, an analog to digital converter, an intelligent sensor, and/or a programmable logic component; where detection of a parameter from the set of collection band parameters includes detecting a trend value for the at least one signal being beyond an acceptable range of trend values; and/or where configuring portions of the system includes implementing a smart band data collection template associated with the detected parameter.
An example procedure for data collection in an industrial environment includes an operation to collect data from one or more sensors configured to sense a condition of an industrial machine in the environment; an operation to check the collected data against a set of criteria that define an acceptable range of the condition; and an operation, in response to the collected data being outside the acceptable range of the condition, to collect data from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a smart band data collection template. In certain embodiments, an example procedure additionally or alternatively includes one or more of the following operations: where being outside the acceptable range of the condition includes a trend of the data from the one or more sensors approaching a maximum value of the acceptable range; where the smart-band group of sensors is defined by the smart band data collection template; where the smart band data collection template includes at least one of a list of sensors to activate, data from the sensors to collect, duration of collection of data from the sensors, and/or a destination location for storing the collected data; where collecting data from a smart-band group of sensors includes configuring at least one data routing resource of the industrial environment that facilitates routing data from the smart band group of sensors to a number of data collectors; and/or where the set of criteria includes a range of trend values derived by processing the data from the one or more sensors.
An example procedure for data collection in an industrial environment includes an operation to configure a data collection plan to collect data from a number of system sensors distributed throughout a machine in the industrial environment, the data collection plan based on machine structural information and an indication of data needed to produce an operational deflection shape visualization of the machine; an operation to configure data sensing, routing, and collection resources in the environment based on the data collection plan; and an operation to collect data based on the data collection plan. In certain embodiments, an example procedure additionally or alternatively includes one or more of the following operations: producing the operational deflection shape visualization based on the collected data; where configuring data sensing, routing, and collection resources is in response to a condition in the environment being detected which is outside of an acceptable range of condition values; where the condition is sensed by a sensor identified in the data collection plan; where the configuring data sensing, routing, and collection resources includes configuring a signal switching resource to concurrently connect the number of system sensors to data collection resources; and/or where the signal switching resource is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform operational deflection shape visualization.
An example system for data collection in an industrial environment includes a number of sensors disposed throughout the environment, a multiplexer that connects signals from the number of sensors to data collection resources, a programmable logic component configured to control the sensors and the multiplexer, an operational deflection shape visualization data collection template that identifies sensors of the number of sensors, a multiplexer configuration of the multiplexer, and at least one programmable logic component control parameter for collection of data for performing operational deflection shape visualization, and a processor for processing data collected from the number of sensors in response to execution of the data collection template, the processing resulting in an operational deflection shape visualization of a portion of a machine disposed in the environment.
Certain further aspects of an example system are described following, any one or more of which may be present in certain embodiments. An example system includes: where the operational deflection shape visualization data collection template further identifies a condition in the environment that triggers performing data collection from the identified sensors; where the condition in the environment is sensed by a sensor identified in the operational deflection shape visualization data collection template; where the operational deflection shape visualization data collection template specifies inputs of the multiplexer to concurrently connect to data collection resources; where the multiplexer is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform operational deflection shape visualization; where the operational deflection shape visualization data collection template specifies data collection requirements for performing operational deflection shape visualization for at least one of looseness, soft joints, bending, and/or twisting of a portion of a machine in the industrial environment; and/or where the operational deflection shape visualization data collection template specifies an order and timing of data collection from a number of identified sensors.
An example monitoring system for data collection includes: a data collector including a number of sensors each outputting a respective detection signal; a data storage structured to store a collector route template for the number of sensors, where the collector route template includes a sensor collection routine for defining how the number of sensors are coupled to a number of input channels; a data acquisition and analysis circuit structured to receive detection signals via the number of input channels, where each of the detection signals has a corresponding detection value, and to evaluate the number of detection values with respect to a rule; and where the data collector is configured to modify the sensor collection routine based on the evaluation of the number of detection values with respect to the rule.
Certain further aspects of an example system are described following, any one or more of which may be present in certain embodiments. An example system includes: where the system is deployed in part locally on the data collector and in part on an information technology infrastructure component apart and remote from the collector; where each of the number of sensors is located in an industrial environment and senses a corresponding parameter; where the rule is based on an operational state of a machine with respect to which the number of sensors provides information; where the rule is based on an anticipated state of a machine with respect to which the number of sensors provides information; where the rule is based on a detected fault condition of a machine with respect to which the number of sensors provides information; where an evaluation of the number of detection values is based on operational mode routing collection schemes; where the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and/or a power savings operational mode; where the data collector modifies the sensor collection routine because the data analysis circuit determines a change in operating modes; where the change in operating modes includes a change from an operational mode to an accelerated maintenance mode; where the change in operating modes includes a change from an operational mode to a failure mode analysis mode; where the change in operating modes includes a change from an operational mode to a power-savings mode; where the change in operating modes includes a change from an operational mode to high-performance mode; where the data collector modifies the sensor collection routine based on a sensed change in a mode of operation; where the sensed change is a failure condition; where the sensed change is a performance condition; where the sensed change is a power condition; where the sensed change is a temperature condition; where the sensed change is a vibration condition; where evaluating the number of detection values with respect to a rule is based on a collection routine with respect to a collection parameter; where the parameter is network availability; where the parameter is sensor availability; where the parameter is a time-based collection routine; where the collection routine collects sensor data on a schedule; and/or where the collection routing evaluates sensor data over time.
An example monitoring system for data collection in an industrial environment includes a number of sensors communicatively coupled to a data collector having a controller; a data collection band circuit structured to determine at least one collection parameter for at least one of the number of sensors from which to process output data; a machine learning data analysis circuit structured to receive output data from the at least one of the number of sensors and to learn received output data patterns indicative of a state; and where the data collection band circuit alters the at least one collection parameter for the at least one of the number of sensors based on one or more of the learned received output data patterns and the state.
Certain further aspects of an example monitoring system are described following, any one or more of which may be present in certain embodiments. An example monitoring system includes: where the state corresponds to an outcome relating to a machine in the environment; where the state corresponds to an anticipated outcome relating to a machine in the environment; where the state corresponds to an outcome relating to a process in the environment; where the state corresponds to an anticipated outcome relating to a process in the environment; where the collection parameter is a bandwidth parameter; where the collection parameter is used to govern a multiplexing of a number of the input sensors; where the collection parameter is a timing parameter; where the collection parameter relates to a frequency range; where the collection parameter relates to a granularity of collection of sensor data; where the collection parameter is a storage parameter for the collected data; where the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model; where the model is a physical model, an operational model, or a system model; where the machine learning data analysis circuit is structured to learn received output data patterns based on the state; where the data collection band circuit alters at least one subset of the number of sensors when the learned received output data pattern does not reliably predict the state; and/or where altering the at least one subset comprises discontinuing collection of data from the at least one subset.
An example monitoring device for data collection in an industrial environment includes a number of sensors communicatively coupled to a controller, the controller including: a data collection band circuit structured to determine at least one subset of the number of sensors from which to process output data; a machine learning data analysis circuit structured to receive output data from the at least one subset of the number of sensors and learn received output data patterns indicative of a state; and where the data collection band circuit alters an aspect of the at least one subset of the number of sensors based on one or more of the learned received output data patterns and the state.
Certain further aspects of an example monitoring device are described following, any one or more of which may be present in certain embodiments. An example monitoring device includes: where the aspect that the data collection band circuit alters is a number of data points collected from one or more members of the at least one subset of number of sensors; where the aspect that the data collection band circuit alters is a frequency of data points collected from one or more members of the at least one subset of number of sensors; where the aspect that the data collection band circuit alters is a bandwidth parameter; where the aspect that the data collection band circuit alters is a timing parameter; where the aspect that the data collection band circuit alters relates to a frequency range; where the aspect that the data collection band circuit alters relates to a granularity of collection of sensor data; and/or where the altered aspect is a storage parameter for the collected data.
An example system includes a user interface of a subsystem adapted to collect data in an industrial environment, where the user interface includes: a number of graphical elements representing mechanical portions of an industrial machine, wherein the number of graphical elements is associated with a condition of interest generated by a processor executing a data analysis algorithm; a number of graphical elements representing data collectors in the subsystem adapted to collect data in an industrial environment which collected data used in the data analysis algorithm; and a number of graphical elements representing sensors used to provide the collected data to the data collectors, wherein the graphical elements representing sensors that provide collected that is outside of an acceptable range are indicated through a visual highlight in the user interface.
Certain further aspects of an example system having a user interface are described following, any one or more of which may be present in certain embodiments. An example system includes: where the condition of interest is selected from a list of conditions of interest presented in the user interface; where the condition of interest is a mechanical failure of at least one of the mechanical portions of the industrial machine; where the mechanical portions include at least one of a bearing, a shaft, a rotor, a housing, and/or a linkage of the industrial machine; where a corresponding acceptable range is available for each sensor; where the user interface further includes highlighting data collectors that collected the data that was outside of the acceptable range; and/or a data collection configuration template that facilitates configuring the data collection subsystem to collect the data for calculating the condition of interest.
Detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The terms “a” or “an,” as used herein, are defined as one or more than one. The term “another,” as used herein, is defined as at least a second or more. The terms “including” and/or “having,” as used herein, are defined as comprising (i.e., open transition).
While only a few embodiments of the present disclosure have been shown and described, it will be obvious to those skilled in the art that many changes and modifications may be made thereunto without departing from the spirit and scope of the present disclosure as described in the following claims. All patent applications and patents, both foreign and domestic, and all other publications referenced herein are incorporated herein in their entireties to the full extent permitted by law.
In embodiments, methods and systems are provided for a system for data collection, processing, and utilization in an industrial environment, referred to herein as the platform 100. With reference to
Intelligent systems 118 may include cognitive systems 120, such as enabling a degree of cognitive behavior as a result of the coordination of processing elements, such as mesh, peer-to-peer, ring, serial and other architectures, where one or more node elements is coordinated with other node elements to provide collective, coordinated behavior to assist in processing, communication, data collection, or the like. The MANET 20 depicted in
In embodiments, the local data collection system 102 may include a high-performance, multi-sensor data collector having a number of novel features for collection and processing of analog and other sensor data. In embodiments, a local data collection system 102 may be deployed to the industrial facilities depicted in
In embodiments, the Mux 1104 then connects to the mother (e.g., with 4 simultaneous channels) and daughter (e.g., with 4 additional channels for 8 total channels) analog boards 1110 via cables where some of the signal conditioning (such as hardware integration) occurs. The signals then move from the analog boards 1110 to the anti-aliasing board where some of the potential aliasing is removed. The rest of the aliasing is done on the delta sigma board 1112, which it connects to through cables. The delta sigma board 1112 provides more aliasing protection along with other conditioning and digitizing of the signal. Next, the data moves to the Jennic™ board 1114 for more digitizing as well as communication to a computer 1128 via USB or Ethernet for additional analysis. In embodiments, the Jennic™ board 1114 may be replaced with a pic board 1118 for more advanced and efficient data collection as well as communication. Both the Jennic™ board 1114 and the pic board 1118 may feed to a self-sufficient DAQ 1122. Once the data moves to the computer 1128, display software 1102 can manipulate the data to show trending, spectra, waveform, statistics, and analytics. In some cases there may be dedicated modules for continuous ultrasonic monitoring 1120 or RFID monitoring of an inclinometer in sensor 1130.
In embodiments, the system is meant to take in all types of data from volts to 4-20 mA signals. In embodiments, open formats of data storage and communication may be used. In some instances, certain portions of the system may be proprietary especially some of research and data associated with the analytics and reporting. In embodiments, smart band analysis is a way to break data down into easily analyzed parts that can be combined with other smart bands to make new more simplified yet sophisticated analytics. In embodiments, this unique information is taken, and graphics are used to depict the conditions because picture depictions are more helpful to the user. In embodiments, complicated programs and user interfaces are simplified so that any user can manipulate the data like an expert.
In embodiments, the system in essence works in a big loop. It starts in software with a general user interface. Most, if not all, online systems require the OEM to create or develop the system GUI 1124. In embodiments, rapid route creation takes advantage of hierarchical templates. In embodiments, a GUI is created so any general user can populate the information itself with simple templates. Once the templates are created the user can copy and paste whatever the user needs. In addition, users can develop their own templates for future ease of use and institutionalizing the knowledge. When the user has entered all of the user's information and connected all of the user's sensors, the user can then start the system acquiring data. In some applications, rotating machinery can build up an electric charge which can harm electrical equipment. In embodiments, in order to diminish this charge's effect on the equipment, a unique electrostatic protection for trigger and vibration inputs is placed upfront on the Mux and DAQ hardware in order to dissipate this electric charge as the signal passed from the sensor to the hardware. In embodiments, the Mux and analog board also can offer upfront circuitry and wider traces in high-amperage input capability using solid state relays and design topology that enables the system to handle high amperage inputs if necessary.
In embodiments, an important part at the front of the Mux is up front signal conditioning on Mux for improved signal-to-noise ratio which provides upfront signal conditioning. Most multiplexers are after thoughts and the original equipment manufacturers usually do not worry or even think about the quality of the signal coming from it. As a result, the signals quality can drop as much as 30 dB or more. Every system is only as strong as its weakest link, so no matter if you have a 24 bit DAQ that has a S/N ratio of 110 dB, your signal quality has already been lost through the Mux. If the signal to noise ratio has dropped to 80 dB in the Mux, it may not be much better than a 16-bit system from 20 years ago.
In embodiments, in addition to providing a better signal, the multiplexer also can play a key role in enhancing a system. Truly continuous systems monitor every sensor all the time but these systems are very expensive. Multiplexer systems can usually only monitor a set number of channels at one time and switches from bank to bank from a larger set of sensors. As a result, the sensors not being collected on are not being monitored so if a level increases the user may never know. In embodiments, a multiplexer continuous monitor alarming feature provides a continuous monitoring alarming multiplexer by placing circuitry on the multiplexer that can measure levels against known alarms even when the data acquisition (“DAQ”) is not monitoring the channel. This in essence makes the system continuous without the ability to instantly capture data on the problem like a true continuous system. In embodiments, coupling this capability to alarm with adaptive scheduling techniques for continuous monitoring and the continuous monitoring system's software adapting and adjusting the data collection sequence based on statistics, analytics, data alarms and dynamic analysis the system will be able to quickly collect dynamic spectral data on the alarming sensor very soon after the alarm sounds.
Another restriction of multiplexers is that they often have a limited number of channels. In embodiments, use of distributed complex programmable logic device (“CPLD”) chips with dedicated bus for logic control of multiple Mux and data acquisition sections enables a CPLD to control multiple mux and DAQs so that there is no limit to the number of channels a system can handle. In embodiments, multiplexers and DAQs can stack together offering additional input and output channels to the system.
Besides having limited number of channels, multiplexers also usually can only collect sensors in the same bank. For detailed analysis, this is very limiting as there is tremendous value in being able to review data simultaneously from sensors on the same machine. In embodiments, use of an analog crosspoint switch for collecting variable groups of vibration input channels addresses this issue by using a crosspoint switch which is often used in the phone industry and provides a matrix circuit so the system can access any set of eight channels from the total number of input sensors.
In embodiments, the system provides all the same capabilities as onsite will allow phase-lock-loop band pass tracking filter method for obtaining slow-speed revolutions per minute (“RPM”) and phase for balancing purposes to remotely balance slow speed machinery such as in paper mills as well as offer additional analysis from its data.
In embodiments, ability to control multiple multiplexers with use of distributed CPLD chips with dedicated bus for logic control of multiple Mux and data acquisition sections is enhanced with a hierarchical multiplexer which allows for multiple DAQ to collect data from multiple multiplexers. In embodiments, this allows for faster data collection as well as more channels of simultaneous data collection which enhances analysis. In embodiments, the Mux may be configured slightly to make it portable and use data acquisition parking features, which turns SV3X DAQ into a protect system.
In embodiments, once the signals leave the multiplexer and hierarchical Mux they move to the analog board where there are other enhancements. In embodiments, power-down of analog channels when not in use as well other power-saving measures including powering down of component boards allow the system to power down channels on the mother and the daughter analog boards in order to save power. In embodiments, this can offer the same power saving benefits to a protect system especially if it is battery operated or solar powered. In embodiments, in order to maximize the signal to noise ratio and provide the best data, a peak-detector for auto-scaling routed into a separate A/D will provide the system the highest peak in each set of data so it can rapidly scale the data to that peak. In embodiments, improved integration using both analog and digital methods create an innovative hybrid integration which also improves or maintains the highest possible signal to noise ratio.
In embodiments, a section of the analog board allows routing of a trigger channel, either raw or buffered, into other analog channels. This allows users to route the trigger to any of the channels for analysis and trouble shooting. In embodiments, once the signals leave the analog board, the signals move into the delta-sigma board where precise voltage reference for A/D zero reference offers more accurate direct current sensor data. The delta sigma's high speeds also provide for using higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize antialiasing filter requirements to oversample the data at a higher input which minimizes anti-aliasing requirements. In embodiments, a CPLD may be used as a clock-divider for a delta-sigma A/D to achieve lower sampling rates without the need for digital resampling so the delta-sigma A/D can achieve lower sampling rates without digitally resampling the data.
In embodiments, the data then moves from the delta-sigma board to the Jennic™ board where digital derivation of phase relative to input and trigger channels using on-board timers digitally derives the phase from the input signal and the trigger using on board timers. In embodiments, the Jennic™ board also has the ability to store calibration data and system maintenance repair history data in an on-board card set. In embodiments, the Jennic™ board will enable acquiring long blocks of data at high-sampling rate as opposed to multiple sets of data taken at different sampling rates so it can stream data and acquire long blocks of data for advanced analysis in the future.
In embodiments, after the signal moves through the Jennic™ board it is then transmitted to the computer. Once on the computer, the software has a number of enhancements that improve the systems analytic capabilities. In embodiments, rapid route creation takes advantage of hierarchical templates and provides rapid route creation of all the equipment using simple templates which also speeds up the software deployment. In embodiments, the software will be used to add intelligence to the system. It will start with an expert system GUIs graphical approach to defining smart bands and diagnoses for the expert system, which will offer a graphical expert system with simplified user interface so anyone can develop complex analytics. In embodiments, this user interface will revolve around smart bands, which are a simplified approach to complex yet flexible analytics for the general user. In embodiments, the smart bands will pair with a self-learning neural network for an even more advanced analytical approach. In embodiments, this system will also use the machine's hierarchy for additional analytical insight. One critical part of predictive maintenance is the ability to learn from known information during repairs or inspections. In embodiments, graphical approaches for back calculations may improve the smart bands and correlations based on a known fault or problem.
In embodiments, besides detailed analysis via smart bands, a bearing analysis method is provided. In recent years, there has been a strong drive in industry to save power which has resulted in an influx of variable frequency drives. In embodiments, torsional vibration detection and analysis utilizing transitory signal analysis provides an advanced torsional vibration analysis for a more comprehensive way to diagnose machinery where torsional forces are relevant (such as machinery with rotating components). In embodiments, the system can deploy a number of intelligent capabilities on its own for better data and more comprehensive analysis. In embodiments, this intelligence will start with a smart route where the software's smart route can adapt the sensors it collects simultaneously in order to gain additional correlative intelligence. In embodiments, smart operational data store (“ODS”) allows the system to elect to gather operational deflection shape analysis in order to further examine the machinery condition. In embodiments, besides changing the route, adaptive scheduling techniques for continuous monitoring allow the system to change the scheduled data collected for full spectral analysis across a number (e.g., eight), of correlative channels. The systems intelligence will provide data to enable extended statistics capabilities for continuous monitoring as well as ambient local vibration for analysis that combines ambient temperature and local temperature and vibration levels changes for identifying machinery issues.
Embodiments of the methods and systems disclosed herein may include a self-sufficient DAQ box. In embodiments, a data acquisition device may be controlled by a personal computer (PC) to implement the desired data acquisition commands. In embodiments, the system has the ability to be self-sufficient and can acquire, process, analyze and monitor independent of external PC control. Embodiments of the methods and systems disclosed herein may include secure digital (SD) card storage. In embodiments, significant additional storage capability is provided utilizing an SD card such as cameras, smart phones, and so on. This can prove critical for monitoring applications where critical data can be stored permanently. Also, if a power failure should occur, the most recent data may be stored despite the fact that it was not off-loaded to another system. Embodiments of the methods and systems disclosed herein may include a DAQ system. A current trend has been to make DAQ systems as communicative as possible with the outside world usually in the form of networks including wireless. Whereas in the past it was common to use a dedicated bus to control a DAQ system with either a microprocessor or microcontroller/microprocessor paired with a PC, today the demands for networking are much greater and so it is out of this environment that arises this new design prototype. In embodiments, multiple microprocessor/microcontrollers or dedicated processors may be utilized to carry out various aspects of this increase in DAQ functionality with one or more processor units focused primarily on the communication aspects with the outside world. This negates the need for constantly interrupting the main processes which include the control of the signal conditioning circuits, triggering, raw data acquisition using the A/D, directing the A/D output to the appropriate on-board memory and processing that data. In embodiments, a specialized microcontroller/microprocessor is designated for all communications with the outside. These include USB, Ethernet and wireless with the ability to provide an IP address or addresses in order to host a webpage. All communications with the outside world are then accomplished using a simple text based menu. The usual array of commands (in practice more than a hundred) such as InitializeCard, AcquireData, StopAcquisition, RetrieveCalibration Info, and so on, would be provided. In addition, in embodiments, other intense signal processing activities including resampling, weighting, filtering, and spectrum processing can be performed by dedicated processors such as field-programmable gate array (“FPGAs”), digital signal processor (“DSP”), microprocessors, micro-controllers, or a combination thereof. In embodiments, this subsystem will communicate via a specialized hardware bus with the communication processing section. It will be facilitated with dual-port memory, semaphore logic, and so on. This embodiment will not only provide a marked improvement in efficiency but can significantly improve the processing capability, including the streaming of the data as well other high-end analytical techniques.
Embodiments of the methods and systems disclosed herein may include radio frequency identification (“RF ID”) and inclinometer on accelerometer or RF ID on other sensors so the sensor can tell the system/software what machine/bearing and direction it is attached to and can automatically set it up in the software to store the data without the user telling it. In embodiments, users could, in turn, put the system on any machine or machines and the system would automatically set itself up and be ready for data collection in seconds
Embodiments of the methods and systems disclosed herein may include ultrasonic online monitoring by placing ultrasonic sensors inside transformers, motor control centers, breakers and the like where the system will monitor via a sound spectrum continuously looking for patterns that identify arcing, corona and other electrical issues indicating a break down or issue. In embodiments, an analysis engine will be used in ultrasonic online monitoring as well as identifying other faults by combining this data with other parameters such as vibration, temperature, pressure, heat flux, magnetic fields, electrical fields, currents, voltage, capacitance, inductance, and combinations (e.g., simple ratios) of the same, among many others.
Embodiments of the methods and systems disclosed herein may include use of an analog crosspoint switch for collecting variable groups of vibration input channels. For vibration analysis, it is useful to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or machines) in multiple directions. By obtaining the readings at the same time, for example, the relative phases of the inputs may be compared for the purpose of diagnosing various mechanical faults. Other types of cross channel analyses such as cross-correlation, transfer functions, Operating Deflection Shape (“ODS”) may also be performed. Current systems using conventional fixed bank multiplexers can only compare a limited number of channels (based on the number of channels per bank) that were assigned to a particular group at the time of installation. The only way to provide some flexibility is to either overlap channels or incorporate lots of redundancy in the system both of which can add considerable expense (in some cases an exponential increase in cost versus flexibility). The simplest Mux design selects one of many inputs and routes it into a single output line. A banked design would consist of a group of these simple building blocks, each handling a fixed group of inputs and routing to its respective output. Typically, the inputs are not overlapping so that the input of one Mux grouping cannot be routed into another. Unlike conventional Mux chips which typically switch a fixed group or banks of a fixed selection of channels into a single output (e.g. in groups of 2, 4, 8, etc.), a crosspoint Mux allows the user to assign any input to any output. Previously, crosspoint multiplexers were used for specialized purposes such as RGB digital video applications and were as a practical matter too noisy for analog applications such as vibration analysis; however more recent advances in the technology now make it feasible. Another advantage of the crosspoint Mux is the ability to disable outputs by putting them into a high impedance state. This is ideal for an output bus so that multiple Mux cards may be stacked and their output buses joined together without the need for bus switches.
Embodiments of the methods and systems disclosed herein may include use of distributed CPLD chips with dedicated bus for logic control of multiple Mux and data acquisition sections. Interfacing to multiple types of predictive maintenance and vibration transducers requires a great deal of switching. This includes AC/DC coupling, 4-20 interfacing, integrated electronic piezoelectric transducer, channel power-down (for conserving op amp power), single-ended or differential grounding options, and so on. Also required is the control of digital pots for range and gain control, switches for hardware integration, AA filtering and triggering. This logic can be performed by a series of CPLD chips strategically located for the tasks they control. A single giant CPLD requires long circuit routes with a great deal of density at the single giant CPLD. In embodiments, distributed CPLDs not only address these concerns but offer a great deal of flexibility. A bus is created where each CPLD that has a fixed assignment has its own unique device address. For multiple boards (e.g., for multiple Mux boards), jumpers are provided for setting multiple addresses. In another example, three bits permit up to 8 boards that are jumper configurable. In embodiments, a bus protocol is defined such that each CPLD on the bus can either be addressed individually or as a group.
Embodiments of the methods and systems disclosed herein may include power-down of analog channels when not in use as well other power-saving measures including powering down of component boards. In embodiments, power-down of analog signal processing op-amps for non-selected channels as well as the ability to power down component boards and other hardware by the low-level firmware for the DAQ system makes high-level application control with respect to power-saving capabilities relatively easy. Explicit control of the hardware is always possible but not required by default.
Embodiments of the methods and systems disclosed herein may include routing of trigger channel either raw or buffered into other analog channels. Many systems have trigger channels for the purposes of determining relative phase between various input data sets or for acquiring significant data without the needless repetition of unwanted input. In embodiments, digitally controlled relays are used to switch either the raw or buffered trigger signal into one of the input channels. Many times, it is extremely useful to examine the quality of the triggering pulse because it is often corrupted for a variety of reasons. These reasons include inadequate placement of the trigger sensor, wiring issues, faulty setup issues such as a dirty piece of reflective tape if using an optical sensor, and so on. The ability to look at either the raw or buffered signal offers an excellent diagnostic or debugging vehicle. It also can offer some improved phase analysis capability by making use of the recorded data signal for various signal processing techniques such as variable speed filtering algorithms.
Embodiments of the methods and systems disclosed herein may include using higher input oversampling for delta-sigma. A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, higher input oversampling rates for delta-sigma A/D are used for lower sampling rate output data to minimize the AA filtering requirements. Lower oversampling rates can be used for higher sampling rates. For example, a 3rd order AA filter set for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) is then adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoff AA filter can then be used for Fmax ranges from 1 kHz and higher (with a secondary filter kicking in at 2.56× the highest sampling rate of 128 kHz). Embodiments of the methods and systems disclosed herein may include use of a CPLD as a clock-divider for a delta-sigma A/D to achieve lower sampling rates without the need for digital resampling. In embodiments, a high-frequency crystal reference can be divided down to lower frequencies by employing a CPLD as a programmable clock divider. The accuracy of the divided down lower frequencies is even more accurate than the original source relative to their longer time periods. This also minimizes or removes the need for resampling processing by the delta-sigma A/D.
Embodiments of the methods and systems disclosed herein may include signal processing firmware/hardware. In embodiments, long blocks of data are acquired at high-sampling rate as opposed to multiple sets of data taken at different sampling rates. Typically, in modern route collection for vibration analysis, it is customary to collect data at a fixed sampling rate with a specified data length. The sampling rate and data length may vary from route point to point based on the specific mechanical analysis requirements at hand. For example, a motor may require a relatively low sampling rate with high resolution to distinguish running speed harmonics from line frequency harmonics. The practical trade-off here though is that it takes more collection time to achieve this improved resolution. In contrast, some high-speed compressors or gear sets require much higher sampling rates to measure the amplitudes of relatively higher frequency data although the precise resolution may not be as necessary. Ideally, however, it would be better to collect a very long sample length of data at a very high sampling rate. When digital acquisition devices first started to be popularized in the early 1980's, the A/D sampling, digital storage, and computational abilities were not close to what they are today, so compromises were made between the time required for data collection and the desired resolution and accuracy. It was because of this limitation that some analysts in the field even refused to give up their analog tape recording systems, which did not suffer as much from these same digitizing drawbacks. A few hybrid systems were employed that would digitize the play back of the recorded analog data at multiple sampling rates and lengths desired, though these systems were admittedly less automated. The more common approach, as mentioned earlier, is to balance data collection time with analysis capability and digitally acquire the data blocks at multiple sampling rates and sampling lengths and digitally store these blocks separately. In embodiments, a long data length of data can be collected at the highest practical sampling rate (e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This long block of data can be acquired in the same amount of time as the shorter length of the lower sampling rates utilized by a priori methods so that there is no effective delay added to the sampling at the measurement point, always a concern in route collection. In embodiments, analog tape recording of data is digitally simulated with such a precision that it can be in effect considered continuous or “analog” for many purposes, including for purposes of embodiments of the present disclosure, except where context indicates otherwise.
Embodiments of the methods and systems disclosed herein may include rapid route creation taking advantage of hierarchical templates. In the field of vibration monitoring, as well as parametric monitoring in general, it is necessary to establish in a database or functional equivalent the existence of data monitoring points. These points are associated a variety of attributes including the following categories: transducer attributes, data collection settings, machinery parameters and operating parameters. The transducer attributes would include probe type, probe mounting type and probe mounting direction or axis orientation. Data collection attributes associated with the measurement would involve a sampling rate, data length, integrated electronic piezoelectric probe power and coupling requirements, hardware integration requirements, 4-20 or voltage interfacing, range and gain settings (if applicable), filter requirements, and so on. Machinery parametric requirements relative to the specific point would include such items as operating speed, bearing type, bearing parametric data which for a rolling element bearing includes the pitch diameter, number of balls, inner race, and outer-race diameters. For a tilting pad bearing, this would include the number of pads and so on. For measurement points on a piece of equipment such as a gearbox, needed parameters would include, for example, the number of gear teeth on each of the gears. For induction motors, it would include the number of rotor bars and poles; for compressors, the number of blades and/or vanes; for fans, the number of blades. For belt/pulley systems, the number of belts as well as the relevant belt-passing frequencies may be calculated from the dimensions of the pulleys and pulley center-to-center distance. For measurements near couplings, the coupling type and number of teeth in a geared coupling may be necessary, and so on. Operating parametric data would include operating load, which may be expressed in megawatts, flow (either air or fluid), percentage, horsepower, feet-per-minute, and so on. Operating temperatures both ambient and operational, pressures, humidity, and so on, may also be relevant. As can be seen, the setup information required for an individual measurement point can be quite large. It is also crucial to performing any legitimate analysis of the data. Machinery, equipment and bearing specific information is essential for identifying fault frequencies as well as anticipating the various kinds of specific faults to be expected. The transducer attributes as well as data collection parameters are vital for properly interpreting the data along with providing limits for the type of analytical techniques suitable. The traditional means of entering this data has been manual and quite tedious, usually at the lowest hierarchical level (for example, at the bearing level with regards to machinery parameters), and at the transducer level for data collection setup information. It cannot be stressed enough, however, the importance of the hierarchical relationships necessary to organize data—both for analytical and interpretive purposes as well as the storage and movement of data. Here, we are focusing primarily on the storage and movement of data. By its nature, the aforementioned setup information is extremely redundant at the level of the lowest hierarchies. However, because of its strong hierarchical nature, it can be stored quite efficiently in that form. In embodiments, hierarchical nature can be utilized when copying data in the form of templates. As an example, hierarchical storage structure suitable for many purposes is defined from general to specific of company, plant or site, unit or process, machine, equipment, shaft element, bearing, and transducer. It is much easier to copy data associated with a particular machine, piece of equipment, shaft element or bearing than it is to copy only at the lowest transducer level. In embodiments, the system not only stores data in this hierarchical fashion, but robustly supports the rapid copying of data using these hierarchical templates. Similarity of elements at specific hierarchical levels lends itself to effective data storage in hierarchical format. For example, so many machines have common elements such as motors, gearboxes, compressors, belts, fans, and so on. More specifically, many motors can be easily classified as induction, DC, fixed or variable speed. Many gearboxes can be grouped into commonly occurring groupings such as input/output, input pinion/intermediate pinion/output pinion, 4-posters, and so on. Within a plant or company, there are many similar types of equipment purchased and standardized on for both cost and maintenance reasons. This results in an enormous overlapping of similar types of equipment and, as a result, offers a great opportunity for taking advantage of a hierarchical template approach.
Embodiments of the methods and systems disclosed herein may include smart bands. Smart bands refer to any processed signal characteristics derived from any dynamic input or group of inputs for the purposes of analyzing the data and achieving the correct diagnoses. Furthermore, smart bands may even include mini or relatively simple diagnoses for the purposes of achieving a more robust and complex one. Historically, in the field of mechanical vibration analysis, Alarm Bands have been used to define spectral frequency bands of interest for the purposes of analyzing and/or trending significant vibration patterns. The Alarm Band typically consists of a spectral (amplitude plotted against frequency) region defined between a low and high frequency border. The amplitude between these borders is summed in the same manner for which an overall amplitude is calculated. A Smart Band is more flexible in that it not only refers to a specific frequency band but can also refer to a group of spectral peaks such as the harmonics of a single peak, a true-peak level or crest factor derived from a time waveform, an overall derived from a vibration envelope spectrum or other specialized signal analysis technique or a logical combination (AND, OR, XOR, etc.) of these signal attributes. In addition, a myriad assortment of other parametric data, including system load, motor voltage and phase information, bearing temperature, flow rates, and the like, can likewise be used as the basis for forming additional smart bands. In embodiments, Smart Band symptoms may be used as building blocks for an expert system whose engine would utilize these inputs to derive diagnoses. Some of these mini-diagnoses may then in turn be used as Smart-Band symptoms (smart bands can include even diagnoses) for more generalized diagnoses.
Embodiments of the methods and systems disclosed herein may include a neural net expert system using smart bands. Typical vibration analysis engines are rule-based (i.e. they use a list of expert rules which, when met, trigger specific diagnoses). In contrast, a neural approach utilizes the weighted triggering of multiple input stimuli into smaller analytical engines or neurons which in turn feed a simplified weighted output to other neurons. The output of these neurons can be also classified as smart bands which in turn feed other neurons. This produces a more layered approach to expert diagnosing as opposed to the one-shot approach of a rule-based system. In embodiments, the expert system utilizes this neural approach using smart bands; however, it does not preclude rule-based diagnoses being reclassified as smart bands as further stimuli to be utilized by the expert system. From this point-of-view, it can be overviewed as a hybrid approach, although at the highest level it is essentially neural.
Embodiments of the methods and systems disclosed herein may include use of database hierarchy in analysis. smart band symptoms and diagnoses may be assigned to various hierarchical database levels. For example, a smart band may be called “Looseness” at the bearing level, trigger “Looseness” at the equipment level, and trigger “Looseness” at the machine level. Another example would be having a smart band diagnosis called “Horizontal Plane Phase Flip” across a coupling and generate a smart band diagnosis of “Vertical Coupling Misalignment” at the machine level.
Embodiments of the methods and systems disclosed herein may include expert system GUIs. In embodiments, the system undertakes a graphical approach to defining smart bands and diagnoses for the expert system. The entry of symptoms, rules, or more generally smart bands for creating a particular machine diagnosis, can be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring. The proposed graphical interface consists of four major components: a symptom parts bin, diagnoses bin, tools bin, and graphical wiring area (“GWA”). In embodiments, a symptom parts bin includes various spectral, waveform, envelope and any type of signal processing characteristic or grouping of characteristics such as a spectral peak, spectral harmonic, waveform true-peak, waveform crest-factor, spectral alarm band, and so on. Each part may be assigned additional properties. For example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×, 3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars x running speed, and so on.
In embodiments, a diagnoses bin includes various pre-defined as well as user-defined diagnoses such as misalignment, imbalance, looseness, bearing faults, and so on. Like parts, diagnoses may also be used as parts for the purposes of building more complex diagnoses. In embodiments, a tools bin includes logical operations such as AND, OR, XOR, etc. or other ways of combining the various parts listed above such as Find Max, Find Min, Interpolate, Average, other Statistical Operations, etc. In embodiments, a graphical wiring area includes parts from the parts bin or diagnoses from the diagnoses bin and may be combined using tools to create diagnoses. The various parts, tools and diagnoses will be represented with icons which are simply graphically wired together in the desired manner. Embodiments of the methods and systems disclosed herein may include an expert system GUIs graphical approach to defining smart bands and diagnoses for the Expert System. The entry of symptoms, rules or more generally smart bands, for creating a particular machine diagnosis, can be tedious and time consuming. One means of making the process more expedient and efficient is to provide a graphical means by use of wiring. In embodiments, a graphical interface may consist of four major components: a symptom parts bin, diagnoses bin, tools bin and graphical wiring area (“GWA”). The symptom parts bin consists of various spectral, waveform, envelope and any type of signal processing characteristic or grouping of characteristics such as a spectral peak, spectral harmonic, waveform true-peak, waveform crest-factor, spectral alarm band, and so on. Each part may be assigned additional properties; for example, a spectral peak part may be assigned a frequency or order (multiple) of running speed. Some parts may be pre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×, 3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars x running speed, and so on. The diagnoses bin consists of various pre-defined as well as user-defined diagnoses such as misalignment, imbalance, looseness, bearing faults, and so on. Like parts, diagnoses may also be used as parts for the purposes of building more complex diagnoses. The tools bin consists of logical operations such as AND, OR, XOR, etc., or other ways of combining the various parts listed above such as find fax, find min, interpolate, average, other statistical operations, etc. A GWA may consist of, in general, parts from the parts bin or diagnoses from the diagnoses bin which are wired together using tools to create diagnoses. The various parts, tools and diagnoses will be represented with icons, which are simply graphically wired together in the desired manor.
Embodiments of the methods and systems disclosed herein may include a graphical approach for back-calculation definition. In embodiments, the expert system also provides the opportunity for the system to learn. If one already knows that a unique set of stimuli or smart bands corresponds to a specific fault or diagnosis, then it is possible to back-calculate a set of coefficients that when applied to a future set of similar stimuli would arrive at the same diagnosis. In embodiments, if there are multiple sets of data a best-fit approach may be used. Unlike the smart band GUI, this embodiment will self-generate a wiring diagram. In embodiments, the user may tailor the back-propagation approach settings and use a database browser to match specific sets of data with the desired diagnoses. In embodiments, the desired diagnoses may be created or custom tailored with a smart band GUI. In embodiments, after that, a user may press the GENERATE button and a dynamic wiring of the symptom-to-diagnosis may appear on the screen as it works through the algorithms to achieve the best fit. In embodiments, when complete, a variety of statistics are presented which detail how well the mapping process proceeded. In some cases, no mapping may be achieved if, for example, the input data was all zero or the wrong data (mistakenly assigned) and so on. Embodiments of the methods and systems disclosed herein may include bearing analysis methods. In embodiments, bearing analysis methods may be used in conjunction with a computer aided design (“CAD”), predictive deconvolution, minimum variance distortionless response (“MVDR”) and spectrum sum-of-harmonics.
Embodiments of the methods and systems disclosed herein may include improved integration using both analog and digital methods. When a signal is digitally integrated using software, essentially the spectral low-end frequency data has its amplitude multiplied by a function which quickly blows up as it approaches zero and creates what is known in the industry as a “ski-slope” effect. The amplitude of the ski-slope is essentially the noise floor of the instrument. The simple remedy for this is the traditional hardware integrator, which can perform at signal-to-noise ratios much greater than that of an already digitized signal. It can also limit the amplification factor to a reasonable level so that multiplication by very large numbers is essentially prohibited. However, at high frequencies where the frequency becomes large, the original amplitude which may be well above the noise floor is multiplied by a very small number (1/f) that plunges it well below the noise floor. The hardware integrator has a fixed noise floor that although low floor does not scale down with the now lower amplitude high-frequency data. In contrast, the same digital multiplication of a digitized high-frequency signal also scales down the noise floor proportionally. In embodiments, hardware integration may be used below the point of unity gain where (at a value usually determined by units and/or desired signal to noise ratio based on gain) and software integration may be used above the value of unity gain to produce an ideal result. In embodiments, this integration is performed in the frequency domain. In embodiments, the resulting hybrid data can then be transformed back into a waveform which should be far superior in signal-to-noise ratio when compared to either hardware integrated or software integrated data. In embodiments, the strengths of hardware integration are used in conjunction with those of digital software integration to achieve the maximum signal-to-noise ratio. In embodiments, the first order gradual hardware integrator high pass filter along with curve fitting allow some relatively low frequency data to get through while reducing or eliminating the noise, allowing very useful analytical data that steep filters kill to be salvaged.
Embodiments of the methods and systems disclosed herein may include adaptive scheduling techniques for continuous monitoring. Continuous monitoring is often performed with an up-front Mux whose purpose it is to select a few channels of data among many to feed the hardware signal processing, A/D, and processing components of a DAQ system. This is done primarily out of practical cost considerations. The tradeoff is that all of the points are not monitored continuously (although they may be monitored to a lesser extent via alternative hardware methods). In embodiments, multiple scheduling levels are provided. In embodiments, at the lowest level, which is continuous for the most part, all of the measurement points will be cycled through in round-robin fashion. For example, if it takes 30 seconds to acquire and process a measurement point and there are 30 points, then each point is serviced once every 15 minutes. However, if a point should alarm by whatever criteria the user selects, its priority level can be increased so that it is serviced more often. As there can be multiple grades of severity for each alarm, so can there me multiple levels of priority with regards to monitoring. In embodiments, more severe alarms will be monitored more frequently. In embodiments, a number of additional high-level signal processing techniques can be applied at less frequent intervals. Embodiments may take advantage of the increased processing power of a PC and the PC can temporarily suspend the round-robin route collection (with its multiple tiers of collection) process and stream the required amount of data for a point of its choosing. Embodiments may include various advanced processing techniques such as envelope processing, wavelet analysis, as well as many other signal processing techniques. In embodiments, after acquisition of this data, the DAQ card set will continue with its route at the point it was interrupted. In embodiments, various PC scheduled data acquisitions will follow their own schedules which will be less frequency than the DAQ card route. They may be set up hourly, daily, by number of route cycles (for example, once every 10 cycles) and also increased scheduling-wise based on their alarm severity priority or type of measurement (e.g., motors may be monitored differently than fans).
Embodiments of the methods and systems disclosed herein may include data acquisition parking features. In embodiments, a data acquisition box used for route collection, real time analysis and in general as an acquisition instrument can be detached from its PC (tablet or otherwise) and powered by an external power supply or suitable battery. In embodiments, the data collector still retains continuous monitoring capability and its on-board firmware can implement dedicated monitoring functions for an extended period of time or can be controlled remotely for further analysis. Embodiments of the methods and systems disclosed herein may include extended statistical capabilities for continuous monitoring.
Embodiments of the methods and systems disclosed herein may include ambient sensing plus local sensing plus vibration for analysis. In embodiments, ambient environmental temperature and pressure, sensed temperature and pressure may be combined with long/medium term vibration analysis for prediction of any of a range of conditions or characteristics. Variants may add infrared sensing, infrared thermography, ultrasound, and many other types of sensors and input types in combination with vibration or with each other. Embodiments of the methods and systems disclosed herein may include a smart route. In embodiments, the continuous monitoring system's software will adapt/adjust the data collection sequence based on statistics, analytics, data alarms and dynamic analysis. Typically, the route is set based on the channels the sensors are attached to. In embodiments, with the crosspoint switch, the Mux can combine any input Mux channels to the (e.g., eight) output channels. In embodiments, as channels go into alarm or the system identifies key deviations, it will pause the normal route set in the software to gather specific simultaneous data, from the channels sharing key statistical changes, for more advanced analysis. Embodiments include conducting a smart ODS or smart transfer function.
Embodiments of the methods and systems disclosed herein may include smart ODS and one or more transfer functions. In embodiments, due to a system's multiplexer and crosspoint switch, an ODS, a transfer function, or other special tests on all the vibration sensors attached to a machine/structure can be performed and show exactly how the machine's points are moving in relationship to each other. In embodiments, 40-50 kHz and longer data lengths (e.g., at least one minute) may be streamed, which may reveal different information than what a normal ODS or transfer function will show. In embodiments, the system will be able to determine, based on the data/statistics/analytics to use, the smart route feature that breaks from the standard route and conducts an ODS across a machine, structure or multiple machines and structures that might show a correlation because the conditions/data directs it. In embodiments, for the transfer functions there may be an impact hammer used on one channel and compared against other vibration sensors on the machine. In embodiments, the system may use the condition changes such as load, speed, temperature or other changes in the machine or system to conduct the transfer function. In embodiments, different transfer functions may be compared to each other over time. In embodiments, difference transfer functions may be strung together like a movie that may show how the machinery fault changes, such as a bearing that could show how it moves through the four stages of bearing failure and so on. Embodiments of the methods and systems disclosed herein may include a hierarchical Mux. In embodiments, a hierarchical Mux may allow modularly output of more channels, such as 16, 24 or more to multiple of eight channel card sets, which would allow gathering more simultaneous channels of data for more complex analysis and faster data collection. Methods and systems are disclosed herein for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility.
With reference to
In embodiments, the machine 2020 can further include a housing 2100 that can contain a drive motor 2110 that can drive a shaft 2120. The shaft 2120 can be supported for rotation or oscillation by a set of bearings 2130, such as including a first bearing 2140 and a second bearing 2150. A data collection module 2160 can connect to (or be resident on) the machine 2020. In one example, the data collection module 2160 can be located and accessible through a cloud network facility 2170, can collect the waveform data 2010 from the machine 2020, and deliver the waveform data 2010 to a remote location. A working end 2180 of the drive shaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, a drill, a gear system, a drive system, or other working element, as the techniques described herein can apply to a wide range of machines, equipment, tools, or the like that include rotating or oscillating elements. In other instances, a generator can be substituted for the motor 2110, and the working end of the drive shaft 2120 can direct rotational energy to the generator to generate power, rather than consume it.
In embodiments, the waveform data 2010 can be obtained using a predetermined route format based on the layout of the machine 2020. The waveform data 2010 may include data from the single-axis sensor 2030 and the three-axis sensor 2050. The single-axis sensor 2030 can serve as a reference probe with its one channel of data and can be fixed at the unchanging location 2040 on the machine under survey. The three-axis sensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes) with its three channels of data and can be moved along a predetermined diagnostic route format from one test point to the next test point. In one example, both sensors 2030, 2050 can be mounted manually to the machine 2020 and can connect to a separate portable computer in certain service examples. The reference probe can remain at one location while the user can move the tri-axial vibration probe along the predetermined route, such as from bearing-to-bearing on a machine. In this example, the user is instructed to locate the sensors at the predetermined locations to complete the survey (or portion thereof) of the machine.
With reference to
In further examples, the sensors and data acquisition modules and equipment can be integral to, or resident on, the rotating machine. By way of these examples, the machine can contain many single-axis sensors and many tri-axial sensors at predetermined locations. The sensors can be originally installed equipment and provided by the original equipment manufacturer or installed at a different time in a retrofit application. The data collection module 2160, or the like, can select and use one single-axis sensor and obtain data from it exclusively during the collection of waveform data 2010 while moving to each of the tri-axial sensors. The data collection module 2160 can be resident on the machine 2020 and/or connect via the cloud network facility 2170
With reference to
In embodiments, a second reference sensor can be used, and a fifth channel of data can be collected. As such, the single-axis sensor can be the first channel and tri-axial vibration can occupy the second, the third, and the fourth data channels. This second reference sensor, like the first, can be a single-axis sensor, such as an accelerometer. In embodiments, the second reference sensor, like the first reference sensor, can remain in the same location on the machine for the entire vibration survey on that machine. The location of the first reference sensor (i.e., the single-axis sensor) may be different than the location of the second reference sensors (i.e., another single-axis sensor). In certain examples, the second reference sensor can be used when the machine has two shafts with different operating speeds, with the two reference sensors being located on the two different shafts. In accordance with this example, further single-axis reference sensors can be employed at additional but different unchanging locations associated with the rotating machine.
In embodiments, the waveform data can be transmitted electronically in a gap-five free format at a significantly high rate of sampling for a relatively longer period of time. In one example, the period of time is 60 seconds to 120 seconds. In another example, the rate of sampling is 100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will be appreciated in light of this disclosure that the waveform data can be shown to approximate more closely some of the wealth of data available from previous instances of analog recording of waveform data.
In embodiments, sampling, band selection, and filtering techniques can permit one or more portions of a long stream of data (i.e., one to two minutes in duration) to be under sampled or over sampled to realize varying effective sampling rates. To this end, interpolation and decimation can be used to further realize varying effective sampling rates. For example, oversampling may be applied to frequency bands that are proximal to rotational or oscillational operating speeds of the sampled machine, or to harmonics thereof, as vibration effects may tend to be more pronounced at those frequencies across the operating range of the machine. In embodiments, the digitally-sampled data set can be decimated to produce a lower sampling rate. It will be appreciated in light of the disclosure that decimate in this context can be the opposite of interpolate. In embodiments, decimating the data set can include first applying a low-pass filter to the digitally-sampled data set and then undersampling the data set.
In one example, a sample waveform at 100 Hz can be undersampled at every tenth point of the digital waveform to produce an effective sampling rate of 10 Hz, but the remaining nine points of that portion of the waveform are effectively discarded and not included in the modeling of the sample waveform. Moreover, this type of bare undersampling can create ghost frequencies due to the undersampling rate (i.e., 10 Hz) relative to the 100 Hz sample waveform.
Most hardware for analog to digital conversions use a sample-and-hold circuit that can charge up a capacitor for a given amount of time such that an average value of the waveform is determined over a specific change in time. It will be appreciated in light of the disclosure that the value of the waveform over the specific change in time in not linear but more similar to a cardinal sinusoidal (“sinc”) function; and, therefore, it can be shown that more emphasis can be placed on the waveform data at the center of the sampling interval with exponential decay of the cardinal sinusoidal signal occurring from its center.
By way of the above example, the sample waveform at 100 Hz can be hardware-sampled at 10 Hz and therefore each sampling point is averaged over 100 milliseconds (e.g., a signal sampled at 100 Hz can have each point averaged over 10 milliseconds). In contrast to the effective discarding of nine out of the ten data points of the sampled waveform as discussed above, the present disclosure can include weighing adjacent data. The adjacent data can include refers to the sample points that were previously discarded and the one remaining point that was retained. In one example, a low pass filter can average the adjacent sample data linearly, i.e., determining the sum of every ten points and then dividing that sum by ten. In a further example, the adjacent data can be weighted with a sinc function. The process of weighting the original waveform with the sinc function can be referred to as an impulse function, or can be referred to in the time domain as a convolution.
The present disclosure can be applicable to not only digitizing a waveform signal based on a detected voltage, but can also be applicable to digitizing waveform signals based on current waveforms, vibration waveforms, and image processing signals including video signal rasterization. In one example, the resizing of a window on a computer screen can be decimated, albeit in at least two directions. In these further examples, it will be appreciated that undersampling by itself can be shown to be insufficient. To that end, oversampling or upsampling by itself can similarly be shown to be insufficient, such that interpolation can be used like decimation but in lieu of only undersampling by itself.
It will be appreciated in light of the disclosure that interpolation in this context can refer to first applying a low pass filter to the digitally-sampled waveform data and then upsampling the waveform data. It will be appreciated in light of the disclosure that real-world examples can often require the use of use non-integer factors for decimation or interpolation, or both. To that end, the present disclosure includes interpolating and decimating sequentially in order to realize a non-integer factor rate for interpolating and decimating. In one example, interpolating and decimating sequentially can define applying a low-pass filter to the sample waveform, then interpolating the waveform after the low-pass filter, and then decimating the waveform after the interpolation. In embodiments, the vibration data can be looped to purposely emulate conventional tape recorder loops, with digital filtering techniques used with the effective splice to facilitate longer analyses. It will be appreciated in light of the disclosure that the above techniques do not preclude waveform, spectrum, and other types of analyses to be processed and displayed with a GUI of the user at the time of collection. It will be appreciated in light of the disclosure that newer systems can permit this functionality to be performed in parallel to the high-performance collection of the raw waveform data.
With respect to time of collection issues, it will be appreciated that older systems using the compromised approach of improving data resolution, by collecting at different sampling rates and data lengths, do not in fact save as much time as expected. To that end, every time the data acquisition hardware is stopped and started, latency issues can be created, especially when there is hardware auto-scaling performed. The same can be true with respect to data retrieval of the route information (i.e., test locations) that is often in a database format and can be exceedingly slow. The storage of the raw data in bursts to disk (whether solid state or otherwise) can also be undesirably slow.
In contrast, the many embodiments include digitally streaming the waveform data 2010, as disclosed herein, and also enjoying the benefit of needing to load the route parameter information while setting the data acquisition hardware only once. Because the waveform data 2010 is streamed to only one file, there is no need to open and close files, or switch between loading and writing operations with the storage medium. It can be shown that the collection and storage of the waveform data 2010, as described herein, can be shown to produce relatively more meaningful data in significantly less time than the traditional batch data acquisition approach. An example of this includes an electric motor about which waveform data can be collected with a data length of 4K points (i.e., 4,096) for sufficiently high resolution in order to, among other things, distinguish electrical sideband frequencies. For fans or blowers, a reduced resolution of 1K (i.e., 1,024) can be used. In certain instances, 1K can be the minimum waveform data length requirement. The sampling rate can be 1,280 Hz and that equates to an Fmax of 500 Hz. It will be appreciated in light of the disclosure that oversampling by an industry standard factor of 2.56 can satisfy the necessary two-times (2×) oversampling for the Nyquist Criterion with some additional leeway that can accommodate anti-aliasing filter-rolloff. The time to acquire this waveform data would be 1,024 points at 1,280 hertz, which are 800 milliseconds.
To improve accuracy, the waveform data can be averaged. Eight averages can be used with, for example, fifty percent overlap. This would extend the time from 800 milliseconds to 3.6 seconds, which is equal to 800 msec x 8 averages x 0.5 (overlap ratio)+0.5×800 msec (non-overlapped head and tail ends). After collection at Fmax=500 Hz waveform data, a higher sampling rate can be used. In one example, ten times (10×) the previous sampling rate can be used and Fmax=10 kHz. By way of this example, eight averages can be used with fifty percent (50%) overlap to collect waveform data at this higher rate that can amount to a collection time of 360 msec or 0.36 seconds. It will be appreciated in light of the disclosure that it can be necessary to read the hardware collection parameters for the higher sampling rate from the route list, as well as permit hardware auto-scaling, or the resetting of other necessary hardware collection parameters, or both. To that end, a few seconds of latency can be added to accommodate the changes in sampling rate. In other instances, introducing latency can accommodate hardware autoscaling and changes to hardware collection parameters that can be required when using the lower sampling rate disclosed herein. In addition to accommodating the change in sampling rate, additional time is needed for reading the route point information from the database (i.e., where to monitor and where to monitor next), displaying the route information, and processing the waveform data. Moreover, display of the waveform data and/or associated spectra can also consume significant time. In light of the above, 15 seconds to 20 seconds can elapse while obtaining waveform data at each measurement point.
In further examples, additional sampling rates can be added but this can make the total amount time for the vibration survey even longer because time adds up from changeover time from one sampling rate to another and from the time to obtain additional data at different sampling rate. In one example, a lower sampling rate is used, such as a sampling rate of 128 Hz where Fmax=50 Hz. By way of this example, the vibration survey would therefore require an additional 36 seconds for the first set of averaged data at this sampling rate, in addition to others mentioned above, and consequently the total time spent at each measurement point increases even more dramatically. Further embodiments include using similar digital streaming of gap free waveform data as disclosed herein for use with wind turbines and other machines that can have relatively slow speed rotating or oscillating systems. In many examples, the waveform data collected can include long samples of data at a relatively high sampling rate. In one example, the sampling rate can be 100 kHz and the sampling duration can be for two minutes on all of the channels being recorded. In many examples, one channel can be for the single-axis reference sensor and three more data channels can be for the tri-axial three channel sensor. It will be appreciated in light of the disclosure that the long data length can be shown to facilitate detection of extremely low frequency phenomena. The long data length can also be shown to accommodate the inherent speed variability in wind turbine operations. Additionally, the long data length can further be shown to provide the opportunity for using numerous averages such as those discussed herein, to achieve very high spectral resolution, and to make feasible tape loops for certain spectral analyses. Many multiple advanced analytical techniques can now become available because such techniques can use the available long uninterrupted length of waveform data in accordance with the present disclosure.
It will also be appreciated in light of the disclosure that the simultaneous collection of waveform data from multiple channels can facilitate performing transfer functions between multiple channels. Moreover, the simultaneous collection of waveform data from multiple channels facilitates establishing phase relationships across the machine so that more sophisticated correlations can be utilized by relying on the fact that the waveforms from each of the channels are collected simultaneously. In other examples, more channels in the data collection can be used to reduce the time it takes to complete the overall vibration survey by allowing for simultaneous acquisition of waveform data from multiple sensors that otherwise would have to be acquired, in a subsequent fashion, moving sensor to sensor in the vibration survey.
The present disclosure includes the use of at least one of the single-axis reference probe on one of the channels to allow for acquisition of relative phase comparisons between channels. The reference probe can be an accelerometer or other type of transducer that is not moved and, therefore, fixed at an unchanging location during the vibration survey of one machine. Multiple reference probes can each be deployed as at suitable locations fixed in place (i.e., at unchanging locations) throughout the acquisition of vibration data during the vibration survey. In certain examples, up to seven reference probes can be deployed depending on the capacity of the data collection module 2160 or the like. Using transfer functions or similar techniques, the relative phases of all channels may be compared with one another at all selected frequencies. By keeping the one or more reference probes fixed at their unchanging locations while moving or monitoring the other tri-axial vibration sensors, it can be shown that the entire machine can be mapped with regard to amplitude and relative phase. This can be shown to be true even when there are more measurement points than channels of data collection. With this information, an operating deflection shape can be created that can show dynamic movements of the machine in 3 D, which can provide an invaluable diagnostic tool. In embodiments, the one or more reference probes can provide relative phase, rather than absolute phase. It will be appreciated in light of the disclosure that relative phase may not be as valuable absolute phase for some purposes, but the relative phase the information can still be shown to be very useful.
In embodiments, the sampling rates used during the vibration survey can be digitally synchronized to predetermined operational frequencies that can relate to pertinent parameters of the machine such as rotating or oscillating speed. Doing this, permits extracting even more information using synchronized averaging techniques. It will be appreciated in light of the disclosure that this can be done without the use of a key phasor or a reference pulse from a rotating shaft, which is usually not available for route collected data. As such, non-synchronous signals can be removed from a complex signal without the need to deploy synchronous averaging using the key phasor. This can be shown to be very powerful when analyzing a particular pinon in a gearbox or generally applied to any component within a complicated mechanical mechanism. In many instances, the key phasor or the reference pulse is rarely available with route collected data, but the techniques disclosed herein can overcome this absence. In embodiments, there can be multiple shafts running at different speeds within the machine being analyzed. In certain instances, there can be a single-axis reference probe for each shaft. In other instances, it is possible to relate the phase of one shaft to another shaft using only one single-axis reference probe on one shaft at its unchanging location. In embodiments, variable speed equipment can be more readily analyzed with relatively longer duration of data relative to single speed equipment. The vibration survey can be conducted at several machine speeds within the same contiguous set of vibration data using the same techniques disclosed herein. These techniques can also permit the study of the change of the relationship between vibration and the change of the rate of speed that was not available before.
In embodiments, there are numerous analytical techniques that can emerge from because raw waveform data can be captured in a gap-free digital format as disclosed herein. The gap-free digital format can facilitate many paths to analyze the waveform data in many ways after the fact to identify specific problems. The vibration data collected in accordance with the techniques disclosed herein can provide the analysis of transient, semi-periodic and very low frequency phenomena. The waveform data acquired in accordance with the present disclosure can contain relatively longer streams of raw gap-free waveform data that can be conveniently played back as needed, and on which many and varied sophisticated analytical techniques can be performed. A large number of such techniques can provide for various forms of filtering to extract low amplitude modulations from transient impact data that can be included in the relatively longer stream of raw gap-free waveform data. It will be appreciated in light of the disclosure that in past data collection practices, these types of phenomena were typically lost by the averaging process of the spectral processing algorithms because the goal of the previous data acquisition module was purely periodic signals; or these phenomena were lost to file size reduction methodologies due to the fact that much of the content from an original raw signal was typically discarded knowing it would not be used.
In embodiments, there is a method of monitoring vibration of a machine having at least one shaft supported by a set of bearings. The method includes monitoring a first data channel assigned to a single-axis sensor at an unchanging location associated with the machine. The method also includes monitoring a second, third, and fourth data channel assigned to a three-axis sensor. The method further includes recording gap-free digital waveform data simultaneously from all of the data channels while the machine is in operation; and determining a change in relative phase based on the digital waveform data. The method also includes the tri-axial sensor being located at a plurality of positions associated with the machine while obtaining the digital waveform. In embodiments, the second, third, and fourth channels are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine. In embodiments, the data is received from all of the sensors on all of their channels simultaneously.
The method also includes determining an operating deflection shape based on the change in relative phase information and the waveform data. In embodiments, the unchanging location of the reference sensor is a position associated with a shaft of the machine. In embodiments, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings in the machine. In embodiments, the unchanging location is a position associated with a shaft of the machine and, wherein, the tri-axial sensors in the sequence of the tri-axial sensors are each located at different positions and are each associated with different bearings that support the shaft in the machine. The various embodiments include methods of sequentially monitoring vibration or similar process parameters and signals of a rotating or oscillating machine or analogous process machinery from a number of channels simultaneously, which can be known as an ensemble. In various examples, the ensemble can include one to eight channels. In further examples, an ensemble can represent a logical measurement grouping on the equipment being monitored whether those measurement locations are temporary for measurement, supplied by the original equipment manufacturer, retrofit at a later date, or one or more combinations thereof.
In one example, an ensemble can monitor bearing vibration in a single direction. In a further example, an ensemble can monitor three different directions (e.g., orthogonal directions) using a tri-axial sensor. In yet further examples, an ensemble can monitor four or more channels where the first channel can monitor a single-axis vibration sensor, and the second, the third, and the fourth channels can monitor each of the three directions of the tri-axial sensor. In other examples, the ensemble can be fixed to a group of adjacent bearings on the same piece of equipment or an associated shaft. The various embodiments provide methods that include strategies for collecting waveform data from various ensembles deployed in vibration studies or the like in a relatively more efficient manner. The methods also include simultaneously monitoring of a reference channel assigned to an unchanging reference location associated with the ensemble monitoring the machine. The cooperation with the reference channel can be shown to support a more complete correlation of the collected waveforms from the ensembles. The reference sensor on the reference channel can be a single-axis vibration sensor, or a phase reference sensor that can be triggered by a reference location on a rotating shaft or the like. As disclosed herein, the methods can further include recording gap-free digital waveform data simultaneously from all of the channels of each ensemble at a relatively high rate of sampling so as to include all frequencies deemed necessary for the proper analysis of the machinery being monitored while it is in operation. The data from the ensembles can be streamed gap-free to a storage medium for subsequent processing that can be connected to a cloud network facility, a local data link, Bluetooth connectivity, cellular data connectivity, or the like.
In embodiments, the methods disclosed herein include strategies for collecting data from the various ensembles including digital signal processing techniques that can be subsequently applied to data from the ensembles to emphasize or better isolate specific frequencies or waveform phenomena. This can be in contrast with current methods that collect multiple sets of data at different sampling rates, or with different hardware filtering configurations including integration, that provide relatively less post-processing flexibility because of the commitment to these same (known as a priori hardware configurations). These same hardware configurations can also be shown to increase time of the vibration survey due to the latency delays associated with configuring the hardware for each independent test. In embodiments, the methods for collecting data from various ensembles include data marker technology that can be used for classifying sections of streamed data as homogenous and belonging to a specific ensemble. In one example, a classification can be defined as operating speed. In doing so, a multitude of ensembles can be created from what conventional systems would collect as only one. The many embodiments include post-processing analytic techniques for comparing the relative phases of all the frequencies of interest not only between each channel of the collected ensemble but also between all of the channels of all of the ensembles being monitored, when applicable.
With reference to
The first machine 2400 can also have tri-axial (e.g., orthogonal axes) sensors 2480, such as a tri-axial sensor 2482, a tri-axial sensor 2484, and more as needed. In many examples, the tri-axial sensors 2480 can be positioned in the first machine 2400 at locations that allow for the sensing of one of each of the bearing packs in the sets of bearings 2420 that is associated with the rotating or oscillating components of the first machine 2400. The first machine 2400 can also have temperature sensors 2500, such as a temperature sensor 2502, a temperature sensor 2504, and more as needed. The first machine 2400 can also have a tachometer sensor 2510 or more as needed that each detail the RPMs of one of its rotating components. By way of the above example, the first sensor ensemble 2450 can survey the above sensors associated with the first machine 2400. To that end, the first ensemble 2450 can be configured to receive eight channels. In other examples, the first sensor ensemble 2450 can be configured to have more than eight channels, or less than eight channels as needed. In this example, the eight channels include two channels that can each monitor a single-axis reference sensor signal and three channels that can monitor a tri-axial sensor signal. The remaining three channels can monitor two temperature signals and a signal from a tachometer. In one example, the first sensor ensemble 2450 can monitor the single-axis sensor 2462, the single-axis sensor 2464, the tri-axial sensor 2482, the temperature sensor 2502, the temperature sensor 2504, and the tachometer sensor 2510 in accordance with the present disclosure. During a vibration survey on the first machine 2400, the first sensor ensemble 2450 can first monitor the tri-axial sensor 2482 and then move next to the tri-axial sensor 2484.
After monitoring the tri-axial sensor 2484, the first sensor ensemble 2450 can monitor additional tri-axial sensors on the first machine 2400 as needed and that are part of the predetermined route list associated with the vibration survey of the first machine 2400, in accordance with the present disclosure. During this vibration survey, the first sensor ensemble 2450 can continually monitor the single-axis sensor 2462, the single-axis sensor 2464, the two temperature sensors 2502, 2504, and the tachometer sensor 2510 while the first ensemble 2450 can serially monitor the multiple tri-axial sensors 2480 in the pre-determined route plan for this vibration survey.
With reference to
The second machine 2600 can also have tri-axial (e.g., orthogonal axes) sensors 2680, such as a tri-axial sensor 2682, a tri-axial sensor 2684, a tri-axial sensor 2686, and more as needed. In many examples, the tri-axial sensors 2680 can be positioned in the second machine 2600 at locations that allow for the sensing of one of each of the bearing packs in the sets of bearings 2620 that is associated with the rotating or oscillating components of the second machine 2600. The second machine 2600 can also have temperature sensors 2700, such as a temperature sensor 2702, a temperature sensor 2704, and more as needed. The second machine 2600 can also have a tachometer sensor 2710 or more as needed that each detail the RPMs of one of its rotating components.
By way of the above example, the second sensor ensemble 2650 can survey the above sensors associated with the second machine 2600. To that end, the second sensor ensemble 2650 can be configured to receive eight channels. In other examples, the second sensor ensemble 2650 can be configured to have more than eight channels or less than eight channels as needed. In this example, the eight channels include one channel that can monitor a single-axis reference sensor signal and six channels that can monitor two tri-axial sensor signals. The remaining channel can monitor a temperature signal. In one example, the second ensemble 2650 can monitor the single-axis sensor 2662, the tri-axial sensor 2682, the tri-axial sensor 2684, and the temperature sensor 2702. During a vibration survey on the second machine 2600 in accordance with the present disclosure, the second sensor ensemble 2650 can first monitor the tri-axial sensor 2682 simultaneously with the tri-axial sensor 2684 and then move onto the tri-axial sensor 2686 simultaneously with the tri-axial sensor 2688.
After monitoring the tri-axial sensors 2680, the second sensor ensemble 2650 can monitor additional tri-axial sensors (in simultaneous pairs) on the second machine 2600 as needed and that are part of the predetermined route list associated with the vibration survey of the second machine 2600 in accordance with the present disclosure. During this vibration survey, the second sensor ensemble 2650 can continually monitor the single-axis sensor 2662 at its unchanging location and the temperature sensor 2702 while the second sensor ensemble 2650 can serially monitor the multiple tri-axial sensors in the pre-determined route plan for this vibration survey.
With continuing reference to
The many embodiments also include a fourth machine 2950 having rotating or oscillating components 2960, or both, each supported by a set of bearings including a bearing pack 2972, a bearing pack 2974, a bearing pack 2976, and more as needed. The fourth machine 2950 can be also monitored by the third sensor ensemble 2850 when the user moves it to the fourth machine 2950. The many embodiments also include a fifth machine 3000 having rotating or oscillating components 3010, or both. The fifth machine 3000 may not be explicitly monitored by any sensor or any sensor ensembles in operation but it can create vibrations or other impulse energy of sufficient magnitude to be recorded in the data associated with any one the machines 2400, 2600, 2800, 2950 under a vibration survey.
The many embodiments include monitoring the first sensor ensemble 2450 on the first machine 2400 through the predetermined route as disclosed herein. The many embodiments also include monitoring the second sensor ensemble 2650 on the second machine 2600 through the predetermined route. The locations of first machine 2400 being close to second machine 2600 can be included in the contextual metadata of both vibration surveys. The third ensemble 2850 can be moved between third machine 2800, fourth machine 2950, and other suitable machines. The fifth machine 3000 has no sensors onboard as configured, but could be monitored as needed by the third sensor ensemble 2850. The machine fifth 3000 and its operational characteristics can be recorded in the metadata in relation to the vibration surveys on the other machines to note its contribution due to its proximity.
The many embodiments include hybrid database adaptation for harmonizing relational metadata and streaming raw data formats. Unlike older systems that utilized traditional database structure for associating nameplate and operational parameters (sometimes deemed metadata) with individual data measurements that are discrete and relatively simple, it will be appreciated in light of the disclosure that more modern systems can collect relatively larger quantities of raw streaming data with higher sampling rates and greater resolutions. At the same time, it will also be appreciated in light of the disclosure that the network of metadata with which to link and obtain this raw data or correlate with this raw data, or both, is expanding at ever-increasing rates.
In one example, a single overall vibration level can be collected as part of a route or prescribed list of measurement points. This data collected can then be associated with database measurement location information for a point located on a surface of a bearing housing on a specific piece of the machine adjacent to a coupling in a vertical direction. Machinery analysis parameters relevant to the proper analysis can be associated with the point located on the surface. Examples of machinery analysis parameters relevant to the proper analysis can include a running speed of a shaft passing through the measurement point on the surface. Further examples of machinery analysis parameters relevant to the proper analysis can include one of, or a combination of: running speeds of all component shafts for that piece of equipment and/or machine, bearing types being analyzed such as sleeve or rolling element bearings, the number of gear teeth on gears should there be a gearbox, the number of poles in a motor, slip and line frequency of a motor, roller bearing element dimensions, number of fan blades, or the like. Examples of machinery analysis parameters relevant to the proper analysis can further include machine operating conditions such as the load on the machines and whether load is expressed in percentage, wattage, air flow, head pressure, horsepower, and the like. Further examples of machinery analysis parameters include information relevant to adjacent machines that might influence the data obtained during the vibration study.
It will be appreciated in light of the disclosure that the vast array of equipment and machinery types can support many different classifications, each of which can be analyzed in distinctly different ways. For example, some machines, like screw compressors and hammer mills, can be shown to run much noisier and can be expected to vibrate significantly more than other machines. Machines known to vibrate more significantly can be shown to require a change in vibration levels that can be considered acceptable relative to quieter machines.
The present disclosure further includes hierarchical relationships found in the vibrational data collected that can be used to support proper analysis of the data. One example of the hierarchical data includes the interconnection of mechanical componentry such as a bearing being measured in a vibration survey and the relationship between that bearing, including how that bearing connects to a particular shaft on which is mounted a specific pinion within a particular gearbox, and the relationship between the shaft, the pinion, and the gearbox. The hierarchical data can further include in what particular spot within a machinery gear train that the bearing being monitored is located relative to other components in the machine. The hierarchical data can also detail whether the bearing being measured in a machine is in close proximity to another machine whose vibrations may affect what is being measured in the machine that is the subject of the vibration study.
The analysis of the vibration data from the bearing or other components related to one another in the hierarchical data can use table lookups, searches for correlations between frequency patterns derived from the raw data, and specific frequencies from the metadata of the machine. In some embodiments, the above can be stored in and retrieved from a relational database. In embodiments, National Instrument's Technical Data Management Solution (TDMS) file format can be used. The TDMS file format can be optimized for streaming various types of measurement data (i.e., binary digital samples of waveforms), as well as also being able to handle hierarchical metadata.
The many embodiments include a hybrid relational metadata-binary storage approach (HRM-BSA). The HRM-BSA can include a structured query language (SQL) based relational database engine. The structured query language based relational database engine can also include a raw data engine that can be optimized for throughput and storage density for data that is flat and relatively structureless. It will be appreciated in light of the disclosure that benefits can be shown in the cooperation between the hierarchical metadata and the SQL relational database engine. In one example, marker technologies and pointer sign-posts can be used to make correlations between the raw database engine and the SQL relational database engine. Three examples of correlations between the raw database engine and the SQL relational database engine linkages include: (1) pointers from the SQL database to the raw data; (2) pointers from the ancillary metadata tables or similar grouping of the raw data to the SQL database; and (3) independent storage tables outside the domain of either the SQL data base or raw data technologies.
With reference to
The present disclosure can include markers that can be applied to a time mark or a sample length within the raw waveform data. The markers generally fall into two categories: preset or dynamic. The preset markers can correlate to preset or existing operating conditions (e.g., load, head pressure, air flow cubic feet per minute, ambient temperature, RPMs, and the like). These preset markers can be fed into the data acquisition system directly. In certain instances, the preset markers can be collected on data channels in parallel with the waveform data (e.g., waveforms for vibration, current, voltage, etc.). Alternatively, the values for the preset markers can be entered manually.
For dynamic markers such as trending data, it can be important to compare similar data like comparing vibration amplitudes and patterns with a repeatable set of operating parameters. One example of the present disclosure includes one of the parallel channel inputs being a key phasor trigger pulse from an operating shaft that can provide RPM information at the instantaneous time of collection. In this example of dynamic markers, sections of collected waveform data can be marked with appropriate speeds or speed ranges.
The present disclosure can also include dynamic markers that can correlate to data that can be derived from post processing and analytics performed on the sample waveform. In further embodiments, the dynamic markers can also correlate to post-collection derived parameters including RPMs, as well as other operationally derived metrics such as alarm conditions like a maximum RPM. In certain examples, many modern pieces of equipment that are candidates for a vibration survey with the portable data collection systems described herein do not include tachometer information. This can be true because it is not always practical or cost-justifiable to add a tachometer even though the measurement of RPM can be of primary importance for the vibration survey and analysis. It will be appreciated that for fixed speed machinery obtaining an accurate RPM measurement can be less important especially when the approximate speed of the machine can be ascertained before-hand; however, variable-speed drives are becoming more and more prevalent. It will also be appreciated in light of the disclosure that various signal processing techniques can permit the derivation of RPM from the raw data without the need for a dedicated tachometer signal.
In many embodiments, the RPM information can be used to mark segments of the raw waveform data over its collection history. Further embodiments include techniques for collecting instrument data following a prescribed route of a vibration study. The dynamic markers can enable analysis and trending software to utilize multiple segments of the collection interval indicated by the markers (e.g., two minutes) as multiple historical collection ensembles, rather than just one as done in previous systems where route collection systems would historically store data for only one RPM setting. This could, in turn, be extended to any other operational parameter such as load setting, ambient temperature, and the like, as previously described. The dynamic markers, however, that can be placed in a type of index file pointing to the raw data stream can classify portions of the stream in homogenous entities that can be more readily compared to previously collected portions of the raw data stream
The many embodiments include the hybrid relational metadata-binary storage approach that can use the best of pre-existing technologies for both relational and raw data streams. In embodiments, the hybrid relational metadata—binary storage approach can marry them together with a variety of marker linkages. The marker linkages can permit rapid searches through the relational metadata and can allow for more efficient analyses of the raw data using conventional SQL techniques with pre-existing technology. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional database technologies do not provide.
The marker linkages can also permit rapid and efficient storage of the raw data using conventional binary storage and data compression techniques. This can be shown to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional raw data technologies provide such as TDMS (National Instruments), UFF (Universal File Format such as UFF58), and the like. The marker linkages can further permit using the marker technology links where a vastly richer set of data from the ensembles can be amassed in the same collection time as more conventional systems. The richer set of data from the ensembles can store data snapshots associated with predetermined collection criterion and the proposed system can derive multiple snapshots from the collected data streams utilizing the marker technology. In doing so, it can be shown that a relatively richer analysis of the collected data can be achieved. One such benefit can include more trending points of vibration at a specific frequency or order of running speed versus RPM, load, operating temperature, flow rates and the like, which can be collected for a similar time relative to what is spent collecting data with a conventional system.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines, elements of the machines and the environment of the machines including heavy duty machines deployed at a local job site or at distributed job sites under common control. The heavy-duty machines may include earthmoving equipment, heavy duty on-road industrial vehicles, heavy duty off-road industrial vehicles, industrial machines deployed in various settings such as turbines, turbomachinery, generators, pumps, pulley systems, manifold and valve systems, and the like. In embodiments, heavy industrial machinery may also include earth-moving equipment, earth-compacting equipment, hauling equipment, hoisting equipment, conveying equipment, aggregate production equipment, equipment used in concrete construction, and piledriving equipment. In examples, earth moving equipment may include excavators, backhoes, loaders, bulldozers, skid steer loaders, trenchers, motor graders, motor scrapers, crawler loaders, and wheeled loading shovels. In examples, construction vehicles may include dumpers, tankers, tippers, and trailers. In examples, material handling equipment may include cranes, conveyors, forklift, and hoists. In examples, construction equipment may include tunnel and handling equipment, road rollers, concrete mixers, hot mix plants, road making machines (compactors), stone crashers, pavers, slurry seal machines, spraying and plastering machines, and heavy-duty pumps. Further examples of heavy industrial equipment may include different systems such as implement traction, structure, power train, control, and information. Heavy industrial equipment may include many different powertrains and combinations thereof to provide power for locomotion and to also provide power to accessories and onboard functionality. In each of these examples, the platform 100 may deploy the local data collection system 102 into the environment 104 in which these machines, motors, pumps, and the like, operate and directly connected integrated into each of the machines, motors, pumps, and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines in operation and machines in being constructed such as turbine and generator sets like Siemens™ SGT6-5000F™ gas turbine, an SST-900™ steam turbine, an SGen6-1000 ATM generator, and an SGen6-100 ATM generator, and the like. In embodiments, the local data collection system 102 may be deployed to monitor steam turbines as they rotate in the currents caused by hot water vapor that may be directed through the turbine but otherwise generated from a different source such as from gas-fired burners, nuclear cores, molten salt loops and the like. In these systems, the local data collection system 102 may monitor the turbines and the water or other fluids in a closed loop cycle in which water condenses and is then heated until it evaporates again. The local data collection system 102 may monitor the steam turbines separately from the fuel source deployed to heat the water to steam. In examples, working temperatures of steam turbines may be between 500 and 650° C. In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement.
The local data collection system 102 may also be deployed in a gas turbines arrangement and therefore not only monitor the turbine in operation but also monitor the hot combustion gases feed into the turbine that may be in excess of 1,500° C. Because these gases are much hotter than those in steam turbines, the blades may be cooled with air that may flow out of small openings to create a protective film or boundary layer between the exhaust gases and the blades. This temperature profile may be monitored by the local data collection system 102. Gas turbine engines, unlike typical steam turbines, include a compressor, a combustion chamber, and a turbine all of which are journaled for rotation with a rotating shaft. The construction and operation of each of these components may be monitored by the local data collection system 102.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from water turbines serving as rotary engines that may harvest energy from moving water and are used for electric power generation. The type of water turbine or hydro-power selected for a project may be based on the height of standing water, often referred to as head, and the flow, or volume of water, at the site. In this example, a generator may be placed at the top of a shaft that connects to the water turbine. As the turbine catches the naturally moving water in its blade and rotates, the turbine sends rotational power to the generator to generate electrical energy. In doing so, the platform 100 may monitor signals from the generators, the turbines, the local water system, flow controls such as dam windows and sluices. Moreover, the platform 100 may monitor local conditions on the electric grid including load, predicted demand, frequency response, and the like, and include such information in the monitoring and control deployed by platform 100 in these hydroelectric settings.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources. In embodiments, elements in such systems may include transmission lines, heat exchangers, desulphurization scrubbers, pumps, coolers, recuperators, chillers, and the like. In embodiments, certain implementations of turbomachinery, turbines, scroll compressors, and the like may be configured in arrayed control so as to monitor large facilities creating electricity for consumption, providing refrigeration, creating steam for local manufacture and heating, and the like, and that arrayed control platforms may be provided by the provider of the industrial equipment such as Honeywell and their Experion™ PKS platform. In embodiments, the platform 100 may specifically communicate with and integrate the local manufacturer-specific controls and may allow equipment from one manufacturer to communicate with other equipment. Moreover, the platform 100 provides allows for the local data collection system 102 to collect information across systems from many different manufacturers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from marine industrial equipment, marine diesel engines, shipbuilding, oil and gas plants, refineries, petrochemical plant, ballast water treatment solutions, marine pumps and turbines and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from heavy industrial equipment and processes including monitoring one or more sensors. By way of this example, sensors may be devices that may be used to detect or respond to some type of input from a physical environment, such as an electrical, heat, or optical signal. In embodiments, the local data collection system 102 may include multiple sensors such as, without limitation, a temperature sensor, a pressure sensor, a torque sensor, a flow sensor, a heat sensors, a smoke sensor, an arc sensor, a radiation sensor, a position sensor, an acceleration sensor, a strain sensor, a pressure cycle sensor, a pressure sensor, an air temperature sensor, and the like. The torque sensor may encompass a magnetic twist angle sensor. In one example, the torque and speed sensors in the local data collection system 102 may be similar to those discussed in U.S. Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013 and hereby incorporated by reference as if fully set forth herein. In embodiments, one or more sensors may be provided such as a tactile sensor, a biosensor, a chemical sensor, an image sensor, a humidity sensor, an inertial sensor, and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors that may provide signals for fault detection including excessive vibration, incorrect material, incorrect material properties, trueness to the proper size, trueness to the proper shape, proper weight, trueness to balance. Additional fault sensors include those for inventory control and for inspections such as to confirming that parts packaged to plan, parts are to tolerance in a plan, occurrence of packaging damage or stress, and sensors that may indicate the occurrence of shock or damage in transit. Additional fault sensors may include detection of the lack of lubrication, over lubrication, the need for cleaning of the sensor detection window, the need for maintenance due to low lubrication, the need for maintenance due to blocking or reduced flow in a lubrication region, and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 that includes aircraft operations and manufacture including monitoring signals from sensors for specialized applications such as sensors used in an aircraft's Attitude and Heading Reference System (AHRS), such as gyroscopes, accelerometers, and magnetometers. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from image sensors such as semiconductor charge coupled devices (CCDs), active pixel sensors, in complementary metal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as an infrared (IR) sensor, an ultraviolet (UV) sensor, a touch sensor, a proximity sensor, and the like. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors configured for optical character recognition (OCR), reading barcodes, detecting surface acoustic waves, detecting transponders, communicating with home automation systems, medical diagnostics, health monitoring, and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from sensors such as a Micro-Electro-Mechanical Systems (MEMS) sensor, such as ST Microelectronics™ LSM303AH smart MEMS sensor, which may include an ultra-low-power high-performance system-in-package featuring a 3D digital linear acceleration sensor and a 3D digital magnetic sensor.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from additional large machines such as turbines, windmills, industrial vehicles, robots, and the like. These large mechanical machines include multiple components and elements providing multiple subsystems on each machine. Toward that end, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from individual elements such as axles, bearings, belts, buckets, gears, shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums, dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals, sockets, sleeves, valves, wheels, actuators, motors, servomotor, and the like. Many of the machines and their elements may include servomotors. The local data collection system 102 may monitor the motor, the rotary encoder, and the potentiometer of the servomechanism to provide three-dimensional detail of position, placement, and progress of industrial processes.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from gear drives, powertrains, transfer cases, multispeed axles, transmissions, direct drives, chain drives, belt-drives, shaft-drives, magnetic drives, and similar meshing mechanical drives. In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from fault conditions of industrial machines that may include overheating, noise, grinding gears, locked gears, excessive vibration, wobbling, under-inflation, over-inflation, and the like. Operation faults, maintenance indicators, and interactions from other machines may cause maintenance or operational issues may occur during operation, during installation, and during maintenance. The faults may occur in the mechanisms of the industrial machines but may also occur in infrastructure that supports the machine such as its wiring and local installation platforms. In embodiments, the large industrial machines may face different types of fault conditions such as overheating, noise, grinding gears, excessive vibration of machine parts, fan vibration problems, problems with large industrial machines rotating parts.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from industrial machinery including failures that may be caused by premature bearing failure that may occur due to contamination or loss of bearing lubricant. In another example, a mechanical defect such as misalignment of bearings may occur. Many factors may contribute to the failure such as metal fatigue, therefore, the local data collection system 102 may monitor cycles and local stresses. By way of this example, the platform 100 may monitor incorrect operation of machine parts, lack of maintenance and servicing of parts, corrosion of vital machine parts, such as couplings or gearboxes, misalignment of machine parts, and the like. Though the fault occurrences cannot be completely stopped, many industrial breakdowns may be mitigated to reduce operational and financial losses. The platform 100 provides real-time monitoring and predictive maintenance in many industrial environments wherein it has been shown to present a cost-savings over regularly-scheduled maintenance processes that replace parts according to a rigid expiration of time and not actual load and wear and tear on the element or machine. To that end, the platform 10 may provide reminders of, or perform some, preventive measures such as adhering to operating manual and mode instructions for machines, proper lubrication, and maintenance of machine parts, minimizing or eliminating overrun of machines beyond their defined capacities, replacement of worn but still functional parts as needed, properly training the personnel for machine use, and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor multiple signals that may be carried by a plurality of physical, electronic, and symbolic formats or signals. The platform 100 may employ signal processing including a plurality of mathematical, statistical, computational, heuristic, and linguistic representations and processing of signals and a plurality of operations needed for extraction of useful information from signal processing operations such as techniques for representation, modeling, analysis, synthesis, sensing, acquisition, and extraction of information from signals. In examples, signal processing may be performed using a plurality of techniques, including but not limited to transformations, spectral estimations, statistical operations, probabilistic and stochastic operations, numerical theory analysis, data mining, and the like. The processing of various types of signals forms the basis of many electrical or computational process. As a result, signal processing applies to almost all disciplines and applications in the industrial environment such as audio and video processing, image processing, wireless communications, process control, industrial automation, financial systems, feature extraction, quality improvements such as noise reduction, image enhancement, and the like. Signal processing for images may include pattern recognition for manufacturing inspections, quality inspection, and automated operational inspection and maintenance. The platform 100 may employ many pattern recognition techniques including those that may classify input data into classes based on key features with the objective of recognizing patterns or regularities in data. The platform 100 may also implement pattern recognition processes with machine learning operations and may be used in applications such as computer vision, speech and text processing, radar processing, handwriting recognition, CAD systems, and the like. The platform 100 may employ supervised classification and unsupervised classification. The supervised learning classification algorithms may be based to create classifiers for image or pattern recognition, based on training data obtained from different object classes. The unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering. For example, some of the analysis techniques used in unsupervised learning may include K-means clustering, Gaussian mixture models, Hidden Markov models, and the like. The algorithms used in supervised and unsupervised learning methods of pattern recognition enable the use of pattern recognition in various high precision applications. The platform 100 may use pattern recognition in face detection related applications such as security systems, tracking, sports related applications, fingerprint analysis, medical and forensic applications, navigation and guidance systems, vehicle tracking, public infrastructure systems such as transport systems, license plate monitoring, and the like.
In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 using machine learning to enable derivation-based learning outcomes from computers without the need to program them. The platform 100 may, therefore, learn from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include presenting a set of example inputs and desired outputs to the machine learning systems. Unsupervised learning may include the learning algorithm itself structuring its input by methods such as pattern detection and/or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions. In examples, machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), a priori algorithms, and the like. In embodiments, certain machine learning algorithms may be used (such as genetic algorithms defined for solving both constrained and unconstrained optimization problems that may be based on natural selection, the process that drives biological evolution). By way of this example, genetic algorithms may be deployed to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective functions are discontinuous, not differentiable, stochastic, or highly nonlinear. In an example, the genetic algorithm may be used to address problems of mixed integer programming, where some components restricted to being integer-valued. Genetic algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. By way of this example, the machine learning systems may be used to perform intelligent computing based control and be responsive to tasks in a wide variety of systems (such as interactive websites and portals, brain-machine interfaces, online security and fraud detection systems, medical applications such as diagnosis and therapy assistance systems, classification of DNA sequences, and the like). In examples, machine learning systems may be used in advanced computing applications (such as online advertising, natural language processing, robotics, search engines, software engineering, speech and handwriting recognition, pattern matching, game playing, computational anatomy, bioinformatics systems and the like). In an example, machine learning may also be used in financial and marketing systems (such as for user behavior analytics, online advertising, economic estimations, financial market analysis, and the like).
Additional details are provided below in connection with the methods, systems, devices, and components depicted in connection with
Combination of inputs (including selection of what sensors or input sources to turn “on” or “off”) may be performed under the control of machine-based intelligence, such as using a local cognitive input selection system 4004, an optionally remote cognitive input selection system 4014, or a combination of the two. The cognitive input selection systems 4004, 4014 may use intelligence and machine learning capabilities described elsewhere in this disclosure, such as using detected conditions (such as informed by the input sources 116 or sensors), state information (including state information determined by a machine state recognition system 4021 that may determine a state), such as relating to an operational state, an environmental state, a state within a known process or workflow, a state involving a fault or diagnostic condition, or many others. This may include optimization of input selection and configuration based on learning feedback from the learning feedback system 4012, which may include providing training data (such as from the host processing system 112 or from other data collection systems 102 either directly or from the host processing system 112) and may include providing feedback metrics, such as success metrics calculated within the analytic system 4018 of the host processing system 112. For example, if a data stream consisting of a particular combination of sensors and inputs yields positive results in a given set of conditions (such as providing improved pattern recognition, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, or the like), then metrics relating to such results from the analytic system 4018 can be provided via the learning feedback system 4012 to the cognitive input selection systems 4004, 4014 to help configure future data collection to select that combination in those conditions (allowing other input sources to be de-selected, such as by powering down the other sensors). In embodiments, selection and de-selection of sensor combinations, under control of one or more of the cognitive input selection systems 4004, may occur with automated variation, such as using genetic programming techniques, such that over time, based on learning feedback system 4012, such as from the analytic system 4018, effective combinations for a given state or set of conditions are promoted, and less effective combinations are demoted, resulting in progressive optimization and adaptation of the local data collection system to each unique environment. Thus, an automatically adapting, multi-sensor data collection system is provided, where cognitive input selection is used, with feedback, to improve the effectiveness, efficiency, or other performance parameter of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results, energy production or usage, and the like), analytic metrics (such as success in recognizing patterns, making predictions, classifying data, or the like), and local system metrics (such as bandwidth utilization, storage utilization, power consumption, and the like). In embodiments, the analytic system 4018, the state recognition system 4021, the policy automation engine 4032, and the cognitive input selection system 4014 of a host may take data from multiple data collection systems 102, such that optimization (including of input selection) may be undertaken through coordinated operation of multiple systems 102. For example, the cognitive input selection system 4014 may understand that if one data collection system 102 is already collecting vibration data for an X-axis, the X-axis vibration sensor for the other data collection system might be turned off, in favor of getting Y-axis data from the other data collector 102. Thus, through coordinated collection by the host cognitive input selection system 4014, the activity of multiple collectors 102, across a host of different sensors, can provide for a rich data set for the host processing system 112, without wasting energy, bandwidth, storage space, or the like. As noted above, optimization may be based on overall system success metrics, analytic success metrics, and local system metrics, or a combination of the above.
Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. In embodiments, machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on historical data about sequences of state information. For example, where a temperature state of an industrial machine exceeds a certain threshold and is followed by a fault condition, such as breaking down of a set of bearings, that temperature state may be tracked by a pattern recognizer, which may produce an output data structure indicating an anticipated bearing fault state (whenever an input state of a high temperature is recognized). A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, galvanic states, magnetic field states, electrical field states, capacitance states, charge and discharge states, motion, position, and many others. States may comprise combined states, where a data structure includes a series of states, each of which is represented by a place in a byte-like data structure. For example, an industrial machine may be characterized by a genetic structure, such as one that provides pressure, temperature, vibration, and acoustic data, the measurement of which takes one place in the data structure, so that the combined state can be operated on as a byte-like structure, such as for compactly characterizing the current combined state of the machine or environment, or compactly characterizing the anticipated state. This byte-like structure can be used by a state machine for machine learning, such as by pattern recognition that operates on the structure to determine patterns that reflect combined effects of multiple conditions. A wide variety of such structure can be tracked and used, such as in machine learning, representing various combinations, of various length, of the different elements that can be sensed in an industrial environment. In embodiments, byte-like structures can be used in a genetic programming technique, such as by substituting different types of data, or data from varying sources, and tracking outcomes over time, so that one or more favorable structures emerges based on the success of those structures when used in real world situations, such as indicating successful predictions of anticipated states, or achievement of success operational outcomes, such as increased efficiency, successful routing of information, achieving increased profits, or the like. That is, by varying what data types and sources are used in byte-like structures that are used for machine optimization over time, a genetic programming-based machine learning facility can “evolve” a set of data structures, consisting of a favorable mix of data types (e.g., pressure, temperature, and vibration), from a favorable mix of data sources (e.g., temperature is derived from sensor X, while vibration comes from sensor Y), for a given purpose. Different desired outcomes may result in different data structures that are best adapted to support effective achievement of those outcomes over time with application of machine learning and promotion of structures with favorable results for the desired outcome in question by genetic programming. The promoted data structures may provide compact, efficient data for various activities as described throughout this disclosure, including being stored in data pools (which may be optimized by storing favorable data structures that provide the best operational results for a given environment), being presented in data marketplaces (such as being presented as the most effective structures for a given purpose), and the like.
In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, the host processing system 112, such as disposed in the cloud, may include the state recognition system 4021, which may be used to infer or calculate a current state or to determine an anticipated future state relating to the data collection system 102 or some aspect of the environment in which the data collection system 102 is disposed, such as the state of a machine, a component, a workflow, a process, an event (e.g., whether the event has occurred), an object, a person, a condition, a function, or the like Maintaining state information allows the host processing system 112 to undertake analysis, such as in one or more analytic systems 4018, to determine contextual information, to apply semantic and conditional logic, and perform many other functions as enabled by the processing architecture 4024 described throughout this disclosure.
In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, the platform 100 includes (or is integrated with, or included in) the host processing system 112, such as on a cloud platform, a policy automation engine 4032 for automating creation, deployment, and management of policies to IoT devices. Polices, which may include access policies, network usage policies, storage usage policies, bandwidth usage policies, device connection policies, security policies, rule-based policies, role-based polices, and others, may be required to govern the use of IoT devices. For example, as IoT devices may have many different network and data communications to other devices, policies may be needed to indicate to what devices a given device can connect, what data can be passed on, and what data can be received. As billions of devices with countless potential connections are expected to be deployed in the near future, it becomes impossible for humans to configure policies for IoT devices on a connection-by-connection basis. Accordingly, an intelligent policy automation engine 4032 may include cognitive features for creating, configuring, and managing policies. The policy automation engine 4032 may consume information about possible policies, such as from a policy database or library, which may include one or more public sources of available policies. These may be written in one or more conventional policy languages or scripts. The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation, thereby avoiding a remote “takeover” by a hacker. This may be accomplished in turn by automatically finding and applying security policies that bar connection of the control infrastructure of the machine to the Internet, by requiring access authentication, or the like. The policy automation engine 4032 may include cognitive features, such as varying the application of policies, the configuration of policies, and the like (such as based on state information from the state recognition system 4021). The policy automation engine 4032 may take feedback, as from the learning feedback system 4012, such as based on one or more analytic results from the analytic system 4018, such as based on overall system results (such as the extent of security breaches, policy violations, and the like), local results, and analytic results. By variation and selection based on such feedback, the policy automation engine 4032 can, over time, learn to automatically create, deploy, configure, and manage policies across very large numbers of devices, such as managing policies for configuration of connections among IoT devices.
Methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial IoT devices, including on-device sensor fusion and data storage for an industrial IoT device, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. For example, pressure and temperature data may be multiplexed into a data stream that combines pressure and temperature in a time series, such as in a byte-like structure (where time, pressure, and temperature are bytes in a data structure, so that pressure and temperature remain linked in time, without requiring separate processing of the streams by outside systems), or by adding, dividing, multiplying, subtracting, or the like, such that the fused data can be stored on the device. Any of the sensor data types described throughout this disclosure can be fused in this manner and stored in a local data pool, in storage, or on an IoT device, such as a data collector, a component of a machine, or the like.
In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a cognitive system is used for a self-organizing storage system 4028 for the data collection system 102. Sensor data, and in particular analog sensor data, can consume large amounts of storage capacity, in particular where a data collector 102 has multiple sensor inputs onboard or from the local environment. Simply storing all the data indefinitely is not typically a favorable option, and even transmitting all of the data may strain bandwidth limitations, exceed bandwidth permissions (such as exceeding cellular data plan capacity), or the like. Accordingly, storage strategies are needed. These typically include capturing only portions of the data (such as snapshots), storing data for limited time periods, storing portions of the data (such as intermediate or abstracted forms), and the like. With many possible selections among these and other options, determining the correct storage strategy may be highly complex. In embodiments, the self-organizing storage system 4028 may use a cognitive system, based on learning feedback system 4012, and use various metrics from the analytic system 4018 or other system of the host cognitive input selection system 4114, such as overall system metrics, analytic metrics, and local performance indicators. The self-organizing storage system 4028 may automatically vary storage parameters, such as storage locations (including local storage on the data collection system 102, storage on nearby data collection systems 102 (such as using peer-to-peer organization) and remote storage, such as network-based storage), storage amounts, storage duration, type of data stored (including individual sensors or input sources 116, as well as various combined or multiplexed data, such as selected under the cognitive input selection systems 4004, 4014), storage type (such as using RAM, Flash, or other short-term memory versus available hard drive space), storage organization (such as in raw form, in hierarchies, and the like), and others. Variation of the parameters may be undertaken with feedback, so that over time the data collection system 102 adapts its storage of data to optimize itself to the conditions of its environment, such as a particular industrial environment, in a way that results in it storing the data that is needed in the right amounts and of the right type for availability to users.
In embodiments, the local cognitive input selection system 4004 may organize fusion of data for various onboard sensors, external sensors (such as in the local environment) and other input sources 116 to the local collection system 102 into one or more fused data streams, such as using the multiplexer 4002 to create various signals that represent combinations, permutations, mixes, layers, abstractions, data-metadata combinations, and the like of the source analog and/or digital data that is handled by the data collection system 102. The selection of a particular fusion of sensors may be determined locally by the cognitive input selection system 4004, such as based on learning feedback from the learning feedback system 4012, such as various overall system, analytic system and local system results and metrics. In embodiments, the system may learn to fuse particular combinations and permutations of sensors, such as in order to best achieve correct anticipation of state, as indicated by feedback of the analytic system 4018 regarding its ability to predict future states, such as the various states handled by the state recognition system 4021. For example, the cognitive input selection system 4004 may indicate selection of a sub-set of sensors among a larger set of available sensors, and the inputs from the selected sensors may be combined, such as by placing input from each of them into a byte of a defined, multi-bit data structure (such as by taking a signal from each at a given sampling rate or time and placing the result into the byte structure, then collecting and processing the bytes over time), by multiplexing in the multiplexer 4002, such as by additive mixing of continuous signals, and the like. Any of a wide range of signal processing and data processing techniques for combination and fusing may be used, including convolutional techniques, coercion techniques, transformation techniques, and the like. The particular fusion in question may be adapted to a given situation by cognitive learning, such as by having the cognitive input selection system 4004 learn, based on learning feedback system 4012 from results (such as conveyed by the analytic system 4018), such that the local data collection system 102 executes context-adaptive sensor fusion. In embodiments the data collection system 102 may comprise self-organizing storage 4028.
In embodiments, the analytic system 4018 may apply to any of a wide range of analytic techniques, including statistical and econometric techniques (such as linear regression analysis, use similarity matrices, heat map based techniques, and the like), reasoning techniques (such as Bayesian reasoning, rule-based reasoning, inductive reasoning, and the like), iterative techniques (such as feedback, recursion, feed-forward and other techniques), signal processing techniques (such as Fourier and other transforms), pattern recognition techniques (such as Kalman and other filtering techniques), search techniques, probabilistic techniques (such as random walks, random forest algorithms, and the like), simulation techniques (such as random walks, random forest algorithms, linear optimization and the like), and others. This may include computation of various statistics or measures. In embodiments, the analytic system 4018 may be disposed, at least in part, on a data collection system 102, such that a local analytic system can calculate one or more measures, such as relating to any of the items noted throughout this disclosure. For example, measures of efficiency, power utilization, storage utilization, redundancy, entropy, and other factors may be calculated onboard, so that the data collection 102 can enable various cognitive and learning functions noted throughout this disclosure without dependence on a remote (e.g., cloud-based) analytic system.
In embodiments, the host processing system 112, a data collection system 102, or both, may include, connect to, or integrate with, a self-organizing networking system 4031, which may comprise a cognitive system for providing machine-based, intelligent or organization of network utilization for transport of data in a data collection system, such as for handling analog and other sensor data, or other source data, such as among one or more local data collection systems 102 and a host processing system 112. This may include organizing network utilization for source data delivered to data collection systems, for feedback data, such as analytic data provided to or via a learning feedback system 4012, data for supporting a marketplace (such as described in connection with other embodiments), and output data provided via output interfaces and ports 4010 from one or more data collection systems 102.
Methods and systems are disclosed herein for a self-organizing data marketplace for industrial IoT data, including where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. A marketplace may be set up initially to make available data collected from one or more industrial environments, such as presenting data by type, by source, by environment, by machine, by one or more patterns, or the like (such as in a menu or hierarchy). The marketplace may vary the data collected, the organization of the data, the presentation of the data (including pushing the data to external sites, providing links, configuring APIs by which the data may be accessed, and the like), the pricing of the data, or the like, such as under machine learning, which may vary different parameters of any of the foregoing. The machine learning facility may manage all of these parameters by self-organization, such as by varying parameters over time (including by varying elements of the data types presented, the data sourced used to obtain each type of data, the data structures presented (such as byte-like structures, fused or multiplexed structures (such as representing multiple sensor types), and statistical structures (such as representing various mathematical products of sensor information), among others), the pricing for the data, where the data is presented, how the data is presented (such as by APIs, by links, by push messaging, and the like), how the data is stored, how the data is obtained, and the like. As parameters are varied, feedback may be obtained as to measures of success, such as number of views, yield (e.g., price paid) per access, total yield, per unit profit, aggregate profit, and many others, and the self-organizing machine learning facility may promote configurations that improve measures of success and demote configurations that do not, so that, over time, the marketplace is progressively configured to present favorable combinations of data types (e.g., ones that provide robust prediction of anticipated states of particular industrial environments of a given type), from favorable sources (e.g., ones that are reliable, accurate and low priced), with effective pricing (e.g., pricing that tends to provide high aggregate profit from the marketplace). The marketplace may include spiders, web crawlers, and the like to seek input data sources, such as finding data pools, connected IoT devices, and the like that publish potentially relevant data. These may be trained by human users and improved by machine learning in a manner similar to that described elsewhere in this disclosure.
In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data. Referring to
In embodiments, a cognitive data packaging system 4110 of the marketplace 4102 may use machine-based intelligence to package data, such as by automatically configuring packages of data in batches, streams, pools, or the like. In embodiments, packaging may be according to one or more rules, models, or parameters, such as by packaging or aggregating data that is likely to supplement or complement an existing model. For example, operating data from a group of similar machines (such as one or more industrial machines noted throughout this disclosure) may be aggregated together, such as based on metadata indicating the type of data or by recognizing features or characteristics in the data stream that indicate the nature of the data. In embodiments, packaging may occur using machine learning and cognitive capabilities, such as by learning what combinations, permutations, mixes, layers, and the like of input sources 116, sensors, information from data pools 4120 and information from data collection systems 102 are likely to satisfy user requirements or result in measures of success. Learning may be based on learning feedback system 4012, such as based on measures determined in an analytic system 4018, such as system performance measures, data collection measures, analytic measures, and the like. In embodiments, success measures may be correlated to marketplace success measures, such as viewing of packages, engagement with packages, purchase or licensing of packages, payments made for packages, and the like. Such measures may be calculated in an analytic system 4018, including associating particular feedback measures with search terms and other inputs, so that the cognitive packaging system 4110 can find and configure packages that are designed to provide increased value to consumers and increased returns for data suppliers. In embodiments, the cognitive data packaging system 4110 can automatically vary packaging, such as using different combinations, permutations, mixes, and the like, and varying weights applied to given input sources, sensors, data pools and the like, using learning feedback system 4012 to promote favorable packages and de-emphasize less favorable packages. This may occur using genetic programming and similar techniques that compare outcomes for different packages. Feedback may include state information from the state recognition system 4021 (such as about various operating states, and the like), as well as about marketplace conditions and states, such as pricing and availability information for other data sources. Thus, an adaptive cognitive data packaging system 4110 is provided that automatically adapts to conditions to provide favorable packages of data for the marketplace 4102.
In embodiments, a cognitive data pricing system 4112 may be provided to set pricing for data packages. In embodiments, the cognitive data pricing system 4112 may use a set of rules, models, or the like, such as setting pricing based on supply conditions, demand conditions, pricing of various available sources, and the like. For example, pricing for a package may be configured to be set based on the sum of the prices of constituent elements (such as input sources, sensor data, or the like), or to be set based on a rule-based discount to the sum of prices for constituent elements, or the like. Rules and conditional logic may be applied, such as rules that factor in cost factors (such as bandwidth and network usage, peak demand factors, scarcity factors, and the like), rules that factor in utilization parameters (such as the purpose, domain, user, role, duration, or the like for a package) and many others. In embodiments, the cognitive data pricing system 4112 may include fully cognitive, intelligent features, such as using genetic programming including automatically varying pricing and tracking feedback on outcomes. Outcomes on which tracking feedback may be based include various financial yield metrics, utilization metrics and the like that may be provided by calculating metrics in an analytic system 4018 on data from the data transaction system 4114. A distributed ledger 4104 may track the interactions of the cognitive data marketplace 4102
Methods and systems are disclosed herein for self-organizing data pools which may include self-organization of data pools based on utilization and/or yield metrics, including utilization and/or yield metrics that are tracked for a plurality of data pools. The data pools may initially comprise unstructured or loosely structured pools of data that contain data from industrial environments, such as sensor data from or about industrial machines or components. For example, a data pool might take streams of data from various machines or components in an environment, such as turbines, compressors, batteries, reactors, engines, motors, vehicles, pumps, rotors, axles, bearings, valves, and many others, with the data streams containing analog and/or digital sensor data (of a wide range of types), data published about operating conditions, diagnostic and fault data, identifying data for machines or components, asset tracking data, and many other types of data. Each stream may have an identifier in the pool, such as indicating its source, and optionally its type. The data pool may be accessed by external systems, such as through one or more interfaces or APIs (e.g., RESTful APIs), or by data integration elements (such as gateways, brokers, bridges, connectors, or the like), and the data pool may use similar capabilities to get access to available data streams. A data pool may be managed by a self-organizing machine learning facility, which may configure the data pool, such as by managing what sources are used for the pool, managing what streams are available, and managing APIs or other connections into and out of the data pool. The self-organization may take feedback such as based on measures of success that may include measures of utilization and yield. The measures of utilization and yield that may include may account for the cost of acquiring and/or storing data, as well as the benefits of the pool, measured either by profit or by other measures that may include user indications of usefulness, and the like. For example, a self-organizing data pool might recognize that chemical and radiation data for an energy production environment are regularly accessed and extracted, while vibration and temperature data have not been used, in which case the data pool might automatically reorganize, such as by ceasing storage of vibration and/or temperature data, or by obtaining better sources of such data. This automated reorganization can also apply to data structures, such as promoting different data types, different data sources, different data structures, and the like, through progressive iteration and feedback.
In embodiments, a platform is provided having self-organization of data pools based on utilization and/or yield metrics. In embodiments, the data pools 4120 may be self-organizing data pools 4120, such as being organized by cognitive capabilities as described throughout this disclosure. The data pools 4120 may self-organize in response to data from the learning feedback system 4012, such as based on feedback of measures and results, including calculated in an analytic system 4018. Organization may include determining what data or packages of data to store in a pool (such as representing particular combinations, permutations, aggregations, and the like), the structure of such data (such as in flat, hierarchical, linked, or other structures), the duration of storage, the nature of storage media (such as hard disks, flash memory, SSDs, network-based storage, or the like), the arrangement of storage bits, and other parameters. The content and nature of storage may be varied, such that a data pool 4020 may learn and adapt, such as based on states of the host processing system 112, one or more data collection systems 102, storage environment parameters (such as capacity, cost, and performance factors), data collection environment parameters, marketplace parameters, and many others. In embodiments, pools 4020 may learn and adapt, such as by variation of the above and other parameters in response to yield metrics (such as return on investment, optimization of power utilization, optimization of revenue, and the like).
Methods and systems are disclosed herein for training AI models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, and where the AI model operates on sensor data from an industrial environment. As noted above, these models may include operating models for industrial environments, machines, workflows, models for anticipating states, models for predicting fault and optimizing maintenance, models for self-organizing storage (on devices, in data pools and/or in the cloud), models for optimizing data transport (such as for optimizing network coding, network-condition-sensitive routing, and the like), models for optimizing data marketplaces, and many others.
In embodiments, a platform is provided having training AI models based on industry-specific feedback. In embodiments, the various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources 116 (such as relating to optimization of specific machines, devices, components, processes, and the like). Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features (such as for a marketplace 4102 or for other purposes of the host processing system 112) may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving IoT devices (such as an industrial environment). This may include optimization of efficiency (such as in electrical, electromechanical, magnetic, physical, thermodynamic, chemical and other processes and systems), optimization of outputs (such as for production of energy, materials, products, services and other outputs), prediction, avoidance and mitigation of faults (such as in the aforementioned systems and processes), optimization of performance measures (such as returns on investment, yields, profits, margins, revenues and the like), reduction of costs (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, and many others), optimization of benefits (such as relating to safety, satisfaction, health), optimization of work flows (such as optimizing time and resource allocation to processes), and others.
Methods and systems are disclosed herein for a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm. Each member of the swarm may be configured with intelligence, and the ability to coordinate with other members. For example, a member of the swarm may track information about what data other members are handling, so that data collection activities, data storage, data processing, and data publishing can be allocated intelligently across the swarm, taking into account conditions of the environment, capabilities of the members of the swarm, operating parameters, rules (such as from a rules engine that governs the operation of the swarm), and current conditions of the members. For example, among four collectors, one that has relatively low current power levels (such as a low battery), might be temporarily allocated the role of publishing data, because it may receive a dose of power from a reader or interrogation device (such as an RFID reader) when it needs to publish the data. A second collector with good power levels and robust processing capability might be assigned more complex functions, such as processing data, fusing data, organizing the rest of the swarm (including self-organization under machine learning, such that the swarm is optimized over time, including by adjusting operating parameters, rules, and the like based on feedback), and the like. A third collector in the swarm with robust storage capabilities might be assigned the task of collecting and storing a category of data, such as vibration sensor data, that consumes considerable bandwidth. A fourth collector in the swarm, such as one with lower storage capabilities, might be assigned the role of collecting data that can usually be discarded, such as data on current diagnostic conditions, where only data on faults needs to be maintained and passed along. Members of a swarm may connect by peer-to-peer relationships by using a member as a “master” or “hub,” or by having them connect in a series or ring, where each member passes along data (including commands) to the next, and is aware of the nature of the capabilities and commands that are suitable for the preceding and/or next member. The swarm may be used for allocation of storage across it (such as using memory of each memory as an aggregate data store. In these examples, the aggregate data store may support a distributed ledger, which may store transaction data, such as for transactions involving data collected by the swarm, transactions occurring in the industrial environment, or the like. In embodiments, the transaction data may also include data used to manage the swarm, the environment, or a machine or components thereof. The swarm may self-organize, either by machine learning capability disposed on one or more members of the swarm, or based on instructions from an external machine learning facility, which may optimize storage, data collection, data processing, data presentation, data transport, and other functions based on managing parameters that are relevant to each. The machine learning facility may start with an initial configuration and vary parameters of the swarm relevant to any of the foregoing (also including varying the membership of the swarm), such as iterating based on feedback to the machine learning facility regarding measures of success (such as utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, profit measures, and others). Over time, the swarm may be optimized to a favorable configuration to achieve the desired measure of success for an owner, operator, or host of an industrial environment or a machine, component, or process thereof.
In embodiments, as depicted in
Methods and systems are disclosed herein for an industrial IoT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial IoT data. A distributed ledger may distribute storage across devices, using a secure protocol, such as ones used for cryptocurrencies (such as the Blockchain™ protocol used to support the Bitcoin™ currency). A ledger or similar transaction record, which may comprise a structure where each successive member of a chain stores data for previous transactions, and a competition can be established to determine which of alternative data stored data structures is “best” (such as being most complete), can be stored across data collectors, industrial machines or components, data pools, data marketplaces, cloud computing elements, servers, and/or on the IT infrastructure of an enterprise (such as an owner, operator or host of an industrial environment or of the systems disclosed herein). The ledger or transaction may be optimized by machine learning, such as to provide storage efficiency, security, redundancy, or the like.
In embodiments, the cognitive data marketplace 4102 may use a secure architecture for tracking and resolving transactions, such as a distributed ledger 4104, wherein transactions in data packages are tracked in a chained, distributed data structure, such as a Blockchain™, allowing forensic analysis and validation where individual devices store a portion of the ledger representing transactions in data packages. The distributed ledger 4104 may be distributed to IoT devices, to data pools 4020, to data collection systems 102, and the like, so that transaction information can be verified without reliance on a single, central repository of information. The transaction system 4114 may be configured to store data in the distributed ledger 4104 and to retrieve data from it (and from constituent devices) in order to resolve transactions. Thus, a distributed ledger 4104 for handling transactions in data, such as for packages of IoT data, is provided. In embodiments, the self-organizing storage system 4028 may be used for optimizing storage of distributed ledger data, as well as for organizing storage of packages of data, such as IoT data, that can be presented in the marketplace 4102.
Methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing and/or other network conditions. Network sensitivity can include awareness of the price of data transport (such as allowing the system to pull or push data during off-peak periods or within the available parameters of paid data plans), the quality of the network (such as to avoid periods where errors are likely), the quality of environmental conditions (such as delaying transmission until signal quality is good, such as when a collector emerges from a shielded environment, avoiding wasting use of power when seeking a signal when shielded, such as by large metal structures typically of industrial environments), and the like.
Methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment. For example, interfaces can recognize what sensors are available and interfaces and/or processors can be turned on to take input from such sensors, including hardware interfaces that allow the sensors to plug in to the data collector, wireless data interfaces (such as where the collector can ping the sensor, optionally providing some power via an interrogation signal), and software interfaces (such as for handling particular types of data). Thus, a collector that is capable of handling various kinds of data can be configured to adapt to the particular use in a given environment. In embodiments, configuration may be automatic or under machine learning, which may improve configuration by optimizing parameters based on feedback measures over time.
Methods and systems are disclosed herein for self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data. Self-organizing storage may allocate storage based on application of machine learning, which may improve storage configuration based on feedback measure over time. Storage may be optimized by configuring what data types are used (e.g., byte-like structures, structures representing fused data from multiple sensors, structures representing statistics or measures calculated by applying mathematical functions on data, and the like) by configuring compression, by configuring data storage duration, by configuring write strategies (such as by striping data across multiple storage devices, using protocols where one device stores instructions for other devices in a chain, and the like), and by configuring storage hierarchies, such as by providing pre-calculated intermediate statistics to facilitate more rapid access to frequently accessed data items). Thus, highly intelligent storage systems may be configured and optimized, based on feedback, over time.
Methods and systems are disclosed herein for self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment. Network coding, including random linear network coding, can enable highly efficient and reliable transport of large amounts of data over various kinds of networks. Different network coding configurations can be selected, based on machine learning, to optimize network coding and other network transport characteristics based on network conditions, environmental conditions, and other factors, such as the nature of the data being transported, environmental conditions, operating conditions, and the like (including by training a network coding selection model over time based on feedback of measures of success, such as any of the measures described herein).
In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network. A cognitive system may vary one or more parameters for networking, such as network type selection (e.g., selecting among available local, cellular, satellite, Wi-Fi, Bluetooth, NFC, Zigbee and other networks), network selection (such as selecting a specific network, such as one that is known to have desired security features), network coding selection (such as selecting a type of network coding for efficient transport[such as random linear network coding, fixed coding, and others]), network timing selection (such as configuring delivery based on network pricing conditions, traffic and the like), network feature selection (such as selecting cognitive features, security features, and the like), network conditions (such as network quality based on current environmental or operation conditions), network feature selection (such as enabling available authentication, permission and similar systems), network protocol selection (such as among HTTP, IP, TCP/IP, cellular, satellite, serial, packet, streaming, and many other protocols), and others. Given bandwidth constraints, price variations, sensitivity to environmental factors, security concerns, and the like, selecting the optimal network configuration can be highly complex and situation dependent. The self-organizing networking system 4030 may vary combinations and permutations of these parameters while taking input from a learning feedback system 4012 such as using information from the analytic system 4018 about various measures of outcomes. In the many examples, outcomes may include overall system measures, analytic success measures, and local performance indicators. In embodiments, input from a learning feedback system 4012 may include information from various sensors and input sources 116, information from the state recognition system 4021 about states (such as events, environmental conditions, operating conditions, and many others), or other information) or taking other inputs. By variation and selection of alternative configurations of networking parameters in different states, the self-organizing networking system may find configurations that are well-adapted to the environment that is being monitored or controlled by the host system 112, such as the one where one or more data collection systems 102 are located and that are well-adapted to emerging network conditions. Thus, a self-organizing, network-condition-adaptive data collection system is provided.
Referring to
In embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a haptic user interface 4302 is provided as an output for a data collection system 102, such as for handling and providing information for vibration, heat, electrical and/or sound outputs, such as to one or more components of the data collection system 102 or to another system, such as a wearable device, mobile phone, or the like. A data collection system 102 may be provided in a form factor suitable for delivering haptic input to a user, such as by vibrating, warming or cooling, buzzing, or the like, such as being disposed in headgear, an armband, a wristband or watch, a belt, an item of clothing, a uniform, or the like. In such cases, data collection systems 102 may be integrated with gear, uniforms, equipment, or the like worn by users, such as individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may trigger haptic feedback. For example, if a nearby industrial machine is overheating, the haptic interface may alert a user by warming up, or by sending a signal to another device (such as a mobile phone) to warmup. If a system is experiencing unusual vibrations, the haptic interface may vibrate. Thus, through various forms of haptic input, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as in an industrial environment) without requiring them to read messages or divert their visual attention away from the task at hand. The haptic interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the haptic system 4302. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitive haptic system may be provided, where selection of inputs or triggers for haptic feedback, selection of outputs, timing, intensity levels, durations, and other parameters (or weights applied to them) may be varied in a process of variation, promotion, and selection (such as using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive haptic interface for a data collection system 102 is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.
Methods and systems are disclosed herein for a presentation layer for AR/VR industrial glasses, where heat map elements are presented based on patterns and/or parameters in collected data. Methods and systems are disclosed herein for condition-sensitive, self-organized tuning of AR/VR interfaces based on feedback metrics and/or training in industrial environments. In embodiments, any of the data, measures, and the like described throughout this disclosure can be presented by visual elements, overlays, and the like for presentation in the AR/VR interfaces, such as in industrial glasses, on AR/VR interfaces on smart phones or tablets, on AR/VR interfaces on data collectors (which may be embodied in smart phones or tablets), on displays located on machines or components, and/or on displays located in industrial environments.
In embodiments, a platform is provided having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having heat maps 4304 displaying collected data from a data collection system 102 for providing input to a tuned AR/VR interface control system 4308. In embodiments, the heat map interface 4304 is provided as an output for a data collection system 102, such as for handling and providing information for visualization of various sensor data and other data (such as map data, analog sensor data, and other data), such as to one or more components of the data collection system 102 or to another system, such as a mobile device, tablet, dashboard, computer, AR/VR device, or the like. A data collection system 102 may be provided in a form factor suitable for delivering visual input to a user, such as by presenting a map that includes indicators of levels of analog and digital sensor data (such as indicating levels of rotation, vibration, heating or cooling, pressure, and many other conditions). In such cases, data collection systems 102 may be integrated with equipment, or the like that are used by individuals responsible for operating or monitoring an industrial environment. In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like, as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to a heat map. Coordinates may include real world location coordinates (such as geo-location or location on a map of an environment), as well as other coordinates, such as time-based coordinates, frequency-based coordinates, or other coordinates that allow for representation of analog sensor signals, digital signals, input source information, and various combinations, in a map-based visualization, such that colors may represent varying levels of input along the relevant dimensions. For example, if a nearby industrial machine is overheating, the heat map interface may alert a user by showing a machine in bright red. If a system is experiencing unusual vibrations, the heat map interface may show a different color for a visual element for the machine, or it may cause an icon or display element representing the machine to vibrate in the interface, calling attention to the element. Clicking, touching, or otherwise interacting with the map can allow a user to drill down and see underlying sensor or input data that is used as an input to the heat map display. Thus, through various forms of display, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors, such as in an industrial environment, without requiring them to read text-based messages or input. The heat map interface, and selection of what outputs should be provided, may be considered in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the heat map UI 4304. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitive heat map system may be provided, where selection of inputs or triggers for heat map displays, selection of outputs, colors, visual representation elements, timing, intensity levels, durations and other parameters (or weights applied to them) may be varied in a process of variation, promotion and selection (such as using genetic programming) with feedback based on real world responses to feedback in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive heat map interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 112, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.
In embodiments, a platform is provided having automatically tuned AR/VR visualization of data collected by a data collector. In embodiments, a platform is provided having an automatically tuned AR/VR visualization system for visualization of data collected by a data collection system 102, such as where the data collection system 102 has an tuned AR/VR interface control system 4308 or provides input to tuned AR/VR interface control system 4308 (such as a mobile phone positioned in a virtual reality or AR headset, a set of AR glasses, or the like). In embodiments, the tuned AR/VR interface control system 4308 is provided as an output interface of a data collection system 102, such as for handling and providing information for visualization of various sensor data and other data (such as map data, analog sensor data, and other data), such as to one or more components of the data collection system 102 or to another system, such as a mobile device, tablet, dashboard, computer, AR/VR device, or the like. A data collection system 102 may be provided in a form factor suitable for delivering AR or VR visual, auditory, or other sensory input to a user, such as by presenting one or more displays (such as 3D-realistic visualizations, objects, maps, camera overlays, or other overlay elements, maps and the like that include or correspond to indicators of levels of analog and digital sensor data (such as indicating levels of rotation, vibration, heating or cooling, pressure and many other conditions, to input sources 116, or the like). In such cases, data collection systems 102 may be integrated with equipment, or the like that are used by individuals responsible for operating or monitoring an industrial environment.
In embodiments, signals from various sensors or input sources (or selective combinations, permutations, mixes, and the like as managed by one or more of the cognitive input selection systems 4004, 4014) may provide input data to populate, configure, modify, or otherwise determine the AR/VR element. Visual elements may include a wide range of icons, map elements, menu elements, sliders, toggles, colors, shapes, sizes, and the like, for representation of analog sensor signals, digital signals, input source information, and various combinations. In many examples, colors, shapes, and sizes of visual overlay elements may represent varying levels of input along the relevant dimensions for a sensor or combination of sensors. In further examples, if a nearby industrial machine is overheating, an AR element may alert a user by showing an icon representing that type of machine in flashing red color in a portion of the display of a pair of AR glasses. If a system is experiencing unusual vibrations, a virtual reality interface showing visualization of the components of the machine (such as overlaying a camera view of the machine with 3D visualization elements) may show a vibrating component in a highlighted color, with motion, or the like, so that it stands out in a virtual reality environment being used to help a user monitor or service the machine. Clicking, touching, moving eyes toward, or otherwise interacting with a visual element in an AR/VR interface may allow a user to drill down and see underlying sensor or input data that is used as an input to the display. Thus, through various forms of display, a data collection system 102 may inform users of the need to attend to one or more devices, machines, or other factors (such as in an industrial environment), without requiring them to read text-based messages or input or divert attention from the applicable environment (whether it is a real environment with AR features or a virtual environment, such as for simulation, training, or the like).
The AR/VR output interface 4208, and selection and configuration of what outputs or displays should be provided, may be handled in the cognitive input selection systems 4004, 4014. For example, user behavior (such as responses to inputs or displays) may be monitored and analyzed in an analytic system 4018, and feedback may be provided through the learning feedback system 4012, so that AR/VR display signals may be provided based on the right collection or package of sensors and inputs, at the right time and in the right manner, to optimize the effectiveness of the tuned AR/VR interface control system 4308. This may include rule-based or model-based feedback (such as providing outputs that correspond in some logical fashion to the source data that is being conveyed). In embodiments, a cognitively tuned AR/VR interface control system 4308 may be provided, where selection of inputs or triggers for AR/VR display elements, selection of outputs (such as colors, visual representation elements, timing, intensity levels, durations and other parameters [or weights applied to them]) and other parameters of a VR/AR environment may be varied in a process of variation, promotion and selection (such as using genetic programming) with feedback based on real world responses in actual situations or based on results of simulation and testing of user behavior. Thus, an adaptive, tuned AR/VR interface for a data collection system 102, or data collected thereby 102, or data handled by a host processing system 112, is provided, which may learn and adapt feedback to satisfy requirements and to optimize the impact on user behavior and reaction, such as for overall system outcomes, data collection outcomes, analytic outcomes, and the like.
As noted above, methods and systems are disclosed herein for continuous ultrasonic monitoring, including providing continuous ultrasonic monitoring of rotating elements and bearings of an energy production facility. Embodiments include using continuous ultrasonic monitoring of an industrial environment as a source for a cloud-deployed pattern recognizer Embodiments include using continuous ultrasonic monitoring to provide updated state information to a state machine that is used as an input to a cloud-based pattern recognizer Embodiments include making available continuous ultrasonic monitoring information to a user based on a policy declared in a policy engine. Embodiments include storing ultrasonic continuous monitoring data with other data in a fused data structure on an industrial sensor device. Embodiments include making a stream of continuous ultrasonic monitoring data from an industrial environment available as a service from a data marketplace. Embodiments include feeding a stream of continuous ultrasonic data into a self-organizing data pool. Embodiments include training a machine learning model to monitor a continuous ultrasonic monitoring data stream where the model is based on a training set created from human analysis of such a data stream, and is improved based on data collected on performance in an industrial environment. Embodiments include a swarm 4202 of data collection systems 102 that include at least one data collector for continuous ultrasonic monitoring of an industrial environment and at least one other type of data collector. Embodiments include using a distributed ledger to store time-series data from continuous ultrasonic monitoring across multiple devices. Embodiments include collecting a stream of continuous ultrasonic data in a self-organizing data collector. Embodiments include collecting a stream of continuous ultrasonic data in a network-sensitive data collector.
Embodiments include collecting a stream of continuous ultrasonic data in a remotely organized data collector. Embodiments include collecting a stream of continuous ultrasonic data in a data collector having self-organized storage 4028. Embodiments include using self-organizing network coding to transport a stream of ultrasonic data collected from an industrial environment. Embodiments include conveying an indicator of a parameter of a continuously collected ultrasonic data stream via a sensory interface of a wearable device. Embodiments include conveying an indicator of a parameter of a continuously collected ultrasonic data stream via a heat map visual interface of a wearable device. Embodiments include conveying an indicator of a parameter of a continuously collected ultrasonic data stream via an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. Embodiments include taking input from a plurality of analog sensors disposed in an industrial environment, multiplexing the sensors into a multiplexed data stream, feeding the data stream into a cloud-deployed machine learning facility, and training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment. Embodiments include deploying policies by a policy engine that govern what data can be used by what users and for what purpose in cloud-based, machine learning. Embodiments include feeding inputs from multiple devices that have fused, on-device storage of multiple sensor streams into a cloud-based pattern recognizer Embodiments include making an output from a cloud-based machine pattern recognizer that analyzes fused data from remote, analog industrial sensors available as a data service in a data marketplace. Embodiments include using a cloud-based platform to identify patterns in data across a plurality of data pools that contain data published from industrial sensors. Embodiments include training a model to identify preferred sensor sets to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment.
Embodiments include a swarm of data collectors that is governed by a policy that is automatically propagated through the swarm. Embodiments include using a distributed ledger to store sensor fusion information across multiple devices. Embodiments include feeding input from a set of self-organizing data collectors into a cloud-based pattern recognizer that uses data from multiple sensors for an industrial environment. Embodiments include feeding input from a set of network-sensitive data collectors into a cloud-based pattern recognizer that uses data from multiple sensors from the industrial environment. Embodiments include feeding input from a set of remotely organized data collectors into a cloud-based pattern recognizer that determines user data from multiple sensors from the industrial environment. Embodiments include feeding input from a set of data collectors having self-organized storage into a cloud-based pattern recognizer that uses data from multiple sensors from the industrial environment. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport of data fused from multiple sensors in the environment. Embodiments include conveying information formed by fusing inputs from multiple sensors in an industrial data collection system in a multi-sensory interface. Embodiments include conveying information formed by fusing inputs from multiple sensors in an industrial data collection system in a heat map interface. Embodiments include conveying information formed by fusing inputs from multiple sensors in an industrial data collection system in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. Embodiments include providing cloud-based pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. Embodiments include using a policy engine to determine what state information can be used for cloud-based machine analysis. Embodiments include feeding inputs from multiple devices that have fused and on-device storage of multiple sensor streams into a cloud-based pattern recognizer to determine an anticipated state of an industrial environment. Embodiments include making anticipated state information from a cloud-based machine pattern recognizer that analyzes fused data from remote, analog industrial sensors available as a data service in a data marketplace. Embodiments include using a cloud-based pattern recognizer to determine an anticipated state of an industrial environment based on data collected from data pools that contain streams of information from machines in the environment. Embodiments include training a model to identify preferred state information to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment. Embodiments include a swarm of data collectors that feeds a state machine that maintains current state information for an industrial environment. Embodiments include using a distributed ledger to store historical state information for fused sensor states a self-organizing data collector that feeds a state machine that maintains current state information for an industrial environment. Embodiments include a network-sensitive data collector that feeds a state machine that maintains current state information for an industrial environment. Embodiments include a remotely organized data collector that feeds a state machine that maintains current state information for an industrial environment. Embodiments include a data collector with self-organized storage that feeds a state machine that maintains current state information for an industrial environment. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport and maintains anticipated state information for the environment. Embodiments include conveying anticipated state information determined by machine learning in an industrial data collection system in a multi-sensory interface. Embodiments include conveying anticipated state information determined by machine learning in an industrial data collection system in a heat map interface. Embodiments include conveying anticipated state information determined by machine learning in an industrial data collection system in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for a cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices, including a cloud-based policy automation engine for IoT, enabling creation, deployment and management of policies that apply to IoT devices. Embodiments include deploying a policy regarding data usage to an on-device storage system that stores fused data from multiple industrial sensors. Embodiments include deploying a policy relating to what data can be provided to whom in a self-organizing marketplace for IoT sensor data. Embodiments include deploying a policy across a set of self-organizing pools of data that contain data streamed from industrial sensing devices to govern use of data from the pools. Embodiments include training a model to determine what policies should be deployed in an industrial data collection system. Embodiments include deploying a policy that governs how a self-organizing swarm should be organized for a particular industrial environment. Embodiments include storing a policy on a device that governs use of storage capabilities of the device for a distributed ledger. Embodiments include deploying a policy that governs how a self-organizing data collector should be organized for a particular industrial environment. Embodiments include deploying a policy that governs how a network-sensitive data collector should use network bandwidth for a particular industrial environment. Embodiments include deploying a policy that governs how a remotely organized data collector should collect, and make available, data relating to a specified industrial environment. Embodiments include deploying a policy that governs how a data collector should self-organize storage for a particular industrial environment. Embodiments include a system for data collection in an industrial environment with a policy engine for deploying policy within the system and self-organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment with a policy engine for deploying a policy within the system, where a policy applies to how data will be presented in a multi-sensory interface. Embodiments include a system for data collection in an industrial environment with a policy engine for deploying a policy within the system, where a policy applies to how data will be presented in a heat map visual interface. Embodiments include a system for data collection in an industrial environment with a policy engine for deploying a policy within the system, where a policy applies to how data will be presented in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for on-device sensor fusion and data storage for industrial IoT devices, including on-device sensor fusion and data storage for an industrial IoT device, where data from multiple sensors is multiplexed at the device for storage of a fused data stream. Embodiments include a self-organizing marketplace that presents fused sensor data that is extracted from on-device storage of IoT devices. Embodiments include streaming fused sensor information from multiple industrial sensors and from an on-device data storage facility to a data pool. Embodiments include training a model to determine what data should be stored on a device in a data collection environment. Embodiments include a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection, where at least some of the data collectors have on-device storage of fused data from multiple sensors. Embodiments include storing distributed ledger information with fused sensor information on an industrial IoT device. Embodiments include on-device sensor fusion and data storage for a self-organizing industrial data collector. Embodiments include on-device sensor fusion and data storage for a network-sensitive industrial data collector. Embodiments include on-device sensor fusion and data storage for a remotely organized industrial data collector. Embodiments include on-device sensor fusion and self-organizing data storage for an industrial data collector. Embodiments include a system for data collection in an industrial environment with on-device sensor fusion and self-organizing network coding for data transport. Embodiments include a system for data collection with on-device sensor fusion of industrial sensor data, where data structures are stored to support alternative, multi-sensory modes of presentation. Embodiments include a system for data collection with on-device sensor fusion of industrial sensor data, where data structures are stored to support visual heat map modes of presentation. Embodiments include a system for data collection with on-device sensor fusion of industrial sensor data, where data structures are stored to support an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for a self-organizing data marketplace for industrial IoT data, including a self-organizing data marketplace for industrial IoT data, where available data elements are organized in the marketplace for consumption by consumers based on training a self-organizing facility with a training set and feedback from measures of marketplace success. Embodiments include organizing a set of data pools in a self-organizing data marketplace based on utilization metrics for the data pools. Embodiments include training a model to determine pricing for data in a data marketplace. Embodiments include feeding a data marketplace with data streams from a self-organizing swarm of industrial data collectors. Embodiments include using a distributed ledger to store transactional data for a self-organizing marketplace for industrial IoT data. Embodiments include feeding a data marketplace with data streams from self-organizing industrial data collectors. Embodiments include feeding a data marketplace with data streams from a set of network-sensitive industrial data collectors. Embodiments include feeding a data marketplace with data streams from a set of remotely organized industrial data collectors. Embodiments include feeding a data marketplace with data streams from a set of industrial data collectors that have self-organizing storage. Embodiments include using self-organizing network coding for data transport to a marketplace for sensor data collected in industrial environments. Embodiments include providing a library of data structures suitable for presenting data in alternative, multi-sensory interface modes in a data marketplace. Embodiments include providing a library in a data marketplace of data structures suitable for presenting data in heat map visualization Embodiments include providing a library in a data marketplace of data structures suitable for presenting data in interfaces that operate with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for self-organizing data pools, including self-organization of data pools based on utilization and/or yield metrics, including utilization and/or yield metrics that are tracked for a plurality of data pools. Embodiments include training a model to present the most valuable data in a data marketplace, where training is based on industry-specific measures of success. Embodiments include populating a set of self-organizing data pools with data from a self-organizing swarm of data collectors. Embodiments include using a distributed ledger to store transactional information for data that is deployed in data pools, where the distributed ledger is distributed across the data pools. Embodiments include self-organizing of data pools based on utilization and/or yield metrics that are tracked for a plurality of data pools, where the pools contain data from self-organizing data collectors. Embodiments include populating a set of self-organizing data pools with data from a set of network-sensitive data collectors. Embodiments include populating a set of self-organizing data pools with data from a set of remotely organized data collectors. Embodiments include populating a set of self-organizing data pools with data from a set of data collectors having self-organizing storage. Embodiments include a system for data collection in an industrial environment with self-organizing pools for data storage and self-organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment with self-organizing pools for data storage that include a source data structure for supporting data presentation in a multi-sensory interface. Embodiments include a system for data collection in an industrial environment with self-organizing pools for data storage that include a source data structure for supporting data presentation in a heat map interface. Embodiments include a system for data collection in an industrial environment with self-organizing pools for data storage that include source a data structure for supporting data presentation in an interface that operates with self-organized tuning of the interface layer. Embodiments include a self-organizing data marketplace receives the plurality of data pools and is organized based on training a marketplace self-organization with a training set and based on feedback from measures of marketplace success with respect to the plurality of data pools.
As noted above, methods and systems are disclosed herein for training AI models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, where the AI model operates on sensor data from an industrial environment. Embodiments include training a swarm of data collectors based on industry-specific feedback. Embodiments include training an AI model to identify and use available storage locations in an industrial environment for storing distributed ledger information. Embodiments include training a swarm of self-organizing data collectors based on industry-specific feedback. Embodiments include training a network-sensitive data collector based on network and industrial conditions in an industrial environment. Embodiments include training a remote organizer for a remotely organized data collector based on industry-specific feedback measures. Embodiments include training a self-organizing data collector to configure storage based on industry-specific feedback. Embodiments include a system for data collection in an industrial environment with cloud-based training of a network coding model for organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment with cloud-based training of a facility that manages presentation of data in a multi-sensory interface. Embodiments include a system for data collection in an industrial environment with cloud-based training of a facility that manages presentation of data in a heat map interface. Embodiments include a system for data collection in an industrial environment with cloud-based training of a facility that manages presentation of data in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for a self-organized swarm of industrial data collectors, including a self-organizing swarm of industrial data collectors that organize among themselves to optimize data collection based on the capabilities and conditions of the members of the swarm. Embodiments include deploying distributed ledger data structures across a swarm of data. Embodiments include a self-organizing swarm of self-organizing data collectors for data collection in industrial environments. Embodiments include a self-organizing swarm of network-sensitive data collectors for data collection in industrial environments. Embodiments include a self-organizing swarm of network-sensitive data collectors for data collection in industrial environments, where the swarm is also configured for remote organization Embodiments include a self-organizing swarm of data collectors having self-organizing storage for data collection in industrial environments. Embodiments include a system for data collection in an industrial environment with a self-organizing swarm of data collectors and self-organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment with a self-organizing swarm of data collectors that relay information for use in a multi-sensory interface. Embodiments include a system for data collection in an industrial environment with a self-organizing swarm of data collectors that relay information for use in a heat map interface. Embodiments include a system for data collection in an industrial environment with a self-organizing swarm of data collectors that relay information for use in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for an industrial IoT distributed ledger, including a distributed ledger supporting the tracking of transactions executed in an automated data marketplace for industrial IoT data. Embodiments include a self-organizing data collector that is configured to distribute collected information to a distributed ledger. Embodiments include a network-sensitive data collector that is configured to distribute collected information to a distributed ledger based on network conditions. Embodiments include a remotely organized data collector that is configured to distribute collected information to a distributed ledger based on intelligent, remote management of the distribution. Embodiments include a data collector with self-organizing local storage that is configured to distribute collected information to a distributed ledger. Embodiments include a system for data collection in an industrial environment using a distributed ledger for data storage and self-organizing network coding for data transport. Embodiments include a system for data collection in an industrial environment using a distributed ledger for data storage of a data structure supporting a haptic interface for data presentation. Embodiments include a system for data collection in an industrial environment using a distributed ledger for data storage of a data structure supporting a heat map interface for data presentation. Embodiments include a system for data collection in an industrial environment using a distributed ledger for data storage of a data structure supporting an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for a network-sensitive collector, including a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing and/or other network conditions. Embodiments include a remotely organized, network condition-sensitive universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment, including network conditions. Embodiments include a network-condition sensitive data collector with self-organizing storage for data collected in an industrial data collection environment. Embodiments include a network-condition sensitive data collector with self-organizing network coding for data transport in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with a network-sensitive data collector that relays a data structure supporting a haptic wearable interface for data presentation. Embodiments include a system for data collection in an industrial environment with a network-sensitive data collector that relays a data structure supporting a heat map interface for data presentation. Embodiments include a system for data collection in an industrial environment with a network-sensitive data collector that relays a data structure supporting an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for a remotely organized universal data collector that can power up and down sensor interfaces based on need and/or conditions identified in an industrial data collection environment. Embodiments include a remotely organized universal data collector with self-organizing storage for data collected in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with remote control of data collection and self-organizing network coding for data transport. Embodiments include a remotely organized data collector for storing sensor data and delivering instructions for use of the data in a haptic or multi-sensory wearable interface. Embodiments include a remotely organized data collector for storing sensor data and delivering instructions for use of the data in a heat map visual interface. Embodiments include a remotely organized data collector for storing sensor data and delivering instructions for use of the data in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for self-organizing storage for a multi-sensor data collector, including self-organizing storage for a multi-sensor data collector for industrial sensor data. Embodiments include a system for data collection in an industrial environment with self-organizing data storage and self-organizing network coding for data transport. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for translating the data for use in a haptic wearable interface. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for translating the data for use in a heat map presentation interface. Embodiments include a data collector with self-organizing storage for storing sensor data and instructions for translating the data for use in an interface that operates with self-organized tuning of the interface layer.
As noted above, methods and systems are disclosed herein for self-organizing network coding for a multi-sensor data network, including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport and a data structure supporting a haptic wearable interface for data presentation. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport and a data structure supporting a heat map interface for data presentation. Embodiments include a system for data collection in an industrial environment with self-organizing network coding for data transport and self-organized tuning of an interface layer for data presentation.
As noted above, methods and systems are disclosed herein for a haptic or multi-sensory user interface, including a wearable haptic or multi-sensory user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. Embodiments include a wearable haptic user interface for conveying industrial state information from a data collector, with vibration, heat, electrical, and/or sound outputs. Embodiments include a wearable haptic user interface for conveying industrial state information from a data collector, with vibration, heat, electrical, and/or sound outputs. The wearable also has a visual presentation layer for presenting a heat map that indicates a parameter of the data. Embodiments include condition-sensitive, self-organized tuning of AR/VR interfaces and multi-sensory interfaces based on feedback metrics and/or training in industrial environments.
As noted above, methods and systems are disclosed herein for a presentation layer for AR/VR industrial glasses, where heat map elements are presented based on patterns and/or parameters in collected data. Embodiments include condition-sensitive, self-organized tuning of a heat map AR/VR interface based on feedback metrics and/or training in industrial environments. As noted above, methods and systems are disclosed herein for condition-sensitive, self-organized tuning of AR/VR interfaces based on feedback metrics and/or training in industrial environments.
The following illustrative clauses describe certain embodiments of the present disclosure. The data collection system mentioned in the following disclosure may be a local data collection system 102, a host processing system 112 (e.g., using a cloud platform), or a combination of a local system and a host system. In embodiments, a data collection system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having IP front-end-end signal conditioning on a multiplexer for improved signal-to-noise ratio. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having multiplexer continuous monitoring alarming features. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having high-amperage input capability using solid state relays and design topology. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having power-down capability of at least one of an analog sensor channel and of a component board. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having unique electrostatic protection for trigger and vibration inputs. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having precise voltage reference for A/D zero reference.
In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having the routing of a trigger channel that is either raw or buffered into other analog channels. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling.
In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having long blocks of data at a high-sampling rate, as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having storage of calibration data with maintenance history on-board card set. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a neural net expert system using intelligent management of data collection bands.
In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having improved integration using both analog and digital methods.
In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having data acquisition parking features. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having SD card storage. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having smart route changes based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having identification of sensor overload. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having RF identification and an inclinometer.
In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having cloud-based, machine pattern recognition based on the fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a self-organizing collector. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a remotely organized collector. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having the use of an analog crosspoint switch for collecting data having variable groups of analog sensor inputs and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having high-amperage input capability using solid state relays and design topology. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having power-down capability of at least one of an analog sensor channel and of a component board. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having unique electrostatic protection for trigger and vibration inputs. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having precise voltage reference for A/D zero reference. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having routing of a trigger channel that is either raw or buffered into other analog channels. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having storage of calibration data with maintenance history on-board card set. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having data acquisition parking features. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having SD card storage. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having identification of sensor overload. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a remotely organized collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having the use of distributed CPLD chips with dedicated bus for logic control of multiple MUX and data acquisition sections and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having unique electrostatic protection for trigger and vibration inputs. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having precise voltage reference for A/D zero reference. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a phase-lock loop band-pass tracking filter for obtaining slow-speed RPMs and phase information. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having digital derivation of phase relative to input and trigger channels using on-board timers. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a peak-detector for auto-scaling that is routed into a separate analog-to-digital converter for peak detection. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having routing of a trigger channel that is either raw or buffered into other analog channels. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having storage of calibration data with maintenance history on-board card set. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having data acquisition parking features. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having SD card storage. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having identification of sensor overload. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a self-organizing collector. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a remotely organized collector. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having power-down capability for at least one of an analog sensor and a component board and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having storage of calibration data with maintenance history on-board card set. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having data acquisition parking features. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having SD card storage. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having identification of sensor overload. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a self-organizing collector. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a remotely organized collector. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having routing of a trigger channel that is either raw or buffered into other analog channels and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having the use of a CPLD as a clock-divider for a delta-sigma analog-to-digital converter to achieve lower sampling rates without the need for digital resampling. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having storage of calibration data with maintenance history on-board card set. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma. A/D for lower sampling rate outputs to minimize AA filter requirements and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having data acquisition parking features. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having SD card storage. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having identification of sensor overload. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a self-organizing collector. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a remotely organized collector. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having the use of higher input oversampling for delta-sigma A/D for lower sampling rate outputs to minimize AA filter requirements and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having storage of calibration data with maintenance history on-board card set. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having data acquisition parking features. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having SD card storage. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having identification of sensor overload. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a self-organizing collector. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a remotely organized collector. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having long blocks of data at a high-sampling rate as opposed to multiple sets of data taken at different sampling rates and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having data acquisition parking features. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having SD card storage. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having identification of sensor overload. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a self-organizing collector. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a remotely organized collector. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having a rapid route creation capability using hierarchical templates and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having data acquisition parking features. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having SD card storage. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having identification of sensor overload. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a self-organizing collector. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a remotely organized collector. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having intelligent management of data collection bands and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having data acquisition parking features. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having SD card storage. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having identification of sensor overload. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a self-organizing collector. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a remotely organized collector. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having a neural net expert system using intelligent management of data collection bands and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having data acquisition parking features. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having SD card storage. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having identification of sensor overload. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a self-organizing collector. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a remotely organized collector. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having use of a database hierarchy in sensor data analysis and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having data acquisition parking features. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having SD card storage. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having identification of sensor overload. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a self-organizing collector. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a remotely organized collector. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having an expert system GUI graphical approach to defining intelligent data collection bands and diagnoses for the expert system and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having proposed bearing analysis methods. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having torsional vibration detection/analysis utilizing transitory signal analysis. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having data acquisition parking features. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having SD card storage. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having identification of sensor overload. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a self-organizing collector. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a remotely organized collector. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having a graphical approach for back-calculation definition and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having data acquisition parking features. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having SD card storage. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having identification of sensor overload. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a self-organizing collector. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a remotely organized collector. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having improved integration using both analog and digital methods and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having data acquisition parking features. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having SD card storage. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having identification of sensor overload. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a self-organizing collector. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a remotely organized collector. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having adaptive scheduling techniques for continuous monitoring of analog data in a local environment and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having data acquisition parking features. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a self-sufficient data acquisition box. In embodiments, a data collection and processing system is provided having data acquisition parking features and having SD card storage. In embodiments, a data collection and processing system is provided having data acquisition parking features and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having data acquisition parking features and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having data acquisition parking features and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having data acquisition parking features and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having data acquisition parking features and having identification of sensor overload. In embodiments, a data collection and processing system is provided having data acquisition parking features and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having data acquisition parking features and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having data acquisition parking features and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having data acquisition parking features and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having data acquisition parking features and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having data acquisition parking features and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having data acquisition parking features and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having data acquisition parking features and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having data acquisition parking features and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a self-organizing collector. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a remotely organized collector. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having data acquisition parking features and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having data acquisition parking features and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having data acquisition parking features and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having SD card storage. In embodiments, a data collection and processing system is provided having SD card storage and having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having SD card storage and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having SD card storage and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having SD card storage and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having SD card storage and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having SD card storage and having identification of sensor overload. In embodiments, a data collection and processing system is provided having SD card storage and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having SD card storage and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having SD card storage and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having SD card storage and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having SD card storage and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having SD card storage and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having SD card storage and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having SD card storage and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having SD card storage and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having SD card storage and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having SD card storage and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having SD card storage and having a self-organizing collector. In embodiments, a data collection and processing system is provided having SD card storage and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having SD card storage and having a remotely organized collector. In embodiments, a data collection and processing system is provided having SD card storage and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having SD card storage and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having SD card storage and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having SD card storage and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having SD card storage and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having identification of sensor overload. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a self-organizing collector. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a remotely organized collector. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having extended onboard statistical capabilities for continuous monitoring and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having identification of sensor overload. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a self-organizing collector. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a remotely organized collector. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having the use of ambient, local and vibration noise for prediction and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having identification of sensor overload. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a self-organizing collector. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a remotely organized collector. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having smart route changes route based on incoming data or alarms to enable simultaneous dynamic data for analysis or correlation and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having smart ODS and transfer functions. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having identification of sensor overload. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having smart OD S and transfer functions and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having smart OD S and transfer functions and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a self-organizing collector. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a remotely organized collector. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having smart ODS and transfer functions and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having a hierarchical multiplexer. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having identification of sensor overload. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a self-organizing collector. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a remotely organized collector. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having a hierarchical multiplexer and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having RF identification and an inclinometer. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a self-organizing collector. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a remotely organized collector. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having RF identification and an inclinometer and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a self-organizing data marketplace for industrial IoT data. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having training AI models based on industry-specific feedback. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a self-organized swarm of industrial data collectors. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having an IoT distributed ledger. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a self-organizing collector. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a network-sensitive collector. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a remotely organized collector. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a self-organizing storage for a multi-sensor data collector. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a self-organizing network coding for multi-sensor data network. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having heat maps displaying collected data for AR/VR. In embodiments, a data collection and processing system is provided having continuous ultrasonic monitoring and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a self-organizing data marketplace for industrial IoT data. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having training AI models based on industry-specific feedback. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having an IoT distributed ledger. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a self-organizing collector. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a network-sensitive collector. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a remotely organized collector. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having cloud-based, machine pattern recognition based on fusion of remote, analog industrial sensors and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a self-organizing data marketplace for industrial IoT data. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having training AI models based on industry-specific feedback. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having an IoT distributed ledger. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a self-organizing collector. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a network-sensitive collector. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a remotely organized collector. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having cloud-based, machine pattern analysis of state information from multiple analog industrial sensors to provide anticipated state information for an industrial system and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a self-organizing data marketplace for industrial IoT data. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having training AI models based on industry-specific feedback. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having an IoT distributed ledger. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a self-organizing collector. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a network-sensitive collector. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a remotely organized collector. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having cloud-based policy automation engine for IoT, with creation, deployment, and management of IoT devices and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a self-organizing data marketplace for industrial IoT data. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having training AI models based on industry-specific feedback. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having an IoT distributed ledger. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a self-organizing collector. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a network-sensitive collector. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a remotely organized collector. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having on-device sensor fusion and data storage for industrial IoT devices and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data. In embodiments, a platform is provided having a self-organizing data marketplace engine for industrial IoT data and having self-organization of data pools based on utilization and/or yield metrics. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having training AI models based on industry-specific feedback. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having an IoT distributed ledger. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a self-organizing collector. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a network-sensitive collector. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a remotely organized collector. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a self-organizing data marketplace for industrial IoT data and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having training AI models based on industry-specific feedback. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a self-organized swarm of industrial data collectors. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having an IoT distributed ledger. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a self-organizing collector. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a network-sensitive collector. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a remotely organized collector. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a self-organizing storage for a multi-sensor data collector. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a self-organizing network coding for multi-sensor data network. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having heat maps displaying collected data for AR/VR. In embodiments, platform is provided having self-organization of data pools based on utilization and/or yield metrics and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having training AI models based on industry-specific feedback. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having an IoT distributed ledger. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a self-organizing collector. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a network-sensitive collector. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a remotely organized collector. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having training AI models based on industry-specific feedback and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a self-organized swarm of industrial data collectors. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having an IoT distributed ledger. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having a self-organizing collector. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having a network-sensitive collector. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having a remotely organized collector. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a self-organized swarm of industrial data collectors and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a network-sensitive collector. In embodiments, a platform is provided having a network-sensitive collector and having a remotely organized collector. In embodiments, a platform is provided having a network-sensitive collector and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having a network-sensitive collector and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having a network-sensitive collector and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having a network-sensitive collector and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a network-sensitive collector and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a remotely organized collector. In embodiments, a platform is provided having a remotely organized collector and having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having a remotely organized collector and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having a remotely organized collector and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having a remotely organized collector and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a remotely organized collector and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a self-organizing storage for a multi-sensor data collector. In embodiments, a platform is provided having a self-organizing storage for a multi-sensor data collector and having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having a self-organizing storage for a multi-sensor data collector and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having a self-organizing storage for a multi-sensor data collector and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a self-organizing storage for a multi-sensor data collector and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network. In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network and having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical, and/or sound outputs. In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a self-organizing network coding for multi-sensor data network and having automatically tuned AR/VR visualization of data collected by a data collector.
In embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs. In embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs and having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having a wearable haptic user interface for an industrial sensor data collector, with vibration, heat, electrical and/or sound outputs and having automatically tuned AR/VR visualization of data collected by a data collector. In embodiments, a platform is provided having heat maps displaying collected data for AR/VR. In embodiments, a platform is provided having heat maps displaying collected data for AR/VR and having automatically tuned AR/VR visualization of data collected by a data collector.
While only a few embodiments of the present disclosure have been shown and described, it will be obvious to those skilled in the art that many changes and modifications may be made thereunto without departing from the spirit and scope of the present disclosure as described in the following claims. All patent applications and patents, both foreign and domestic, and all other publications referenced herein are incorporated herein in their entireties to the full extent permitted by law.
The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines. In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions, and the like. The processor may be or may include a signal processor, digital processor, embedded processor, microprocessor, or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor, and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more thread. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor, or any machine utilizing one, may include non-transitory memory that stores methods, codes, instructions, and programs as described herein and elsewhere. The processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and the like.
A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).
The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, client, firewall, gateway, hub, router, or other such computer and/or networking hardware. The software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server, cloud server, and other variants such as secondary server, host server, distributed server, and the like. The server may include one or more of memories, processors, computer readable transitory and/or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.
The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.
The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client, and the like. The client may include one or more of memories, processors, computer readable transitory and/or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the client. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.
The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate with existing data collection, processing and storage systems while preserving access to existing format/frequency range/resolution compatible data. While the industrial machine sensor data streaming facilities described herein may collect a greater volume of data (e.g., longer duration of data collection) from sensors at a wider range of frequencies and with greater resolution than existing data collection systems, methods and systems may be employed to provide access to data from the stream of data that represents one or more ranges of frequency and/or one or more lines of resolution that are purposely compatible with existing systems. Further, a portion of the streamed data may be identified, extracted, stored, and/or forwarded to existing data processing systems to facilitate operation of existing data processing systems that substantively matches operation of existing data processing systems using existing collection-based data. In this way, a newly deployed system for sensing aspects of industrial machines, such as aspects of moving parts of industrial machines, may facilitate continued use of existing sensed data processing facilities, algorithms, models, pattern recognizers, user interfaces and the like.
Through identification of existing frequency ranges, formats, and/or resolution, such as by accessing a data structure that defines these aspects of existing data, higher resolution streamed data may be configured to represent a specific frequency, frequency range, format, and/or resolution. This configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data. One approach to adapting streamed data for compatibility with existing sensed data may include aligning the streamed data with existing data so that portions of the streamed data that align with the existing data can be extracted, stored, and made available for processing with existing data processing methods. Alternatively, data processing methods may be configured to process portions of the streamed data that correspond, such as through alignment, to the existing data with methods that implement functions substantially similar to the methods used to process existing data, such as methods that process data that contain a particular frequency range or a particular resolution and the like.
Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range. This method can thusly be at least partially identifiable by these characteristics of the data being processed. Therefore, given a set of conditions, such as moving device being sensed, industrial machine type, frequency of data being sensed, and the like, a data processing system may select an appropriate method. Also, given such as set of conditions, an industrial machine data sensing and processing facility may configure elements, such as data filters, routers, processors, and the like to handle data meeting the conditions.
With regard to
In embodiments, a frequency and/or resolution detection facility 4742 may be configured to facilitate detecting information about legacy instrument sourced data, such as a frequency range of the data or a resolution of the data, and the like. The frequency and/or resolution detection facility 4742 may operate on data directly from the legacy instruments 4730 or from data stored in a legacy data storage facility 4732. The frequency and/or resolution detection facility 4742 may communicate information that it has detected about the legacy instruments 4730, its sourced data, and its data from the legacy data storage facility 4732, or the like to the streaming data collector 4710. Alternatively, the detection facility 4742 may access information, such as information about frequency ranges, resolution and the like that characterizes the sourced data from the legacy instrument 4730 and/or may be accessed from a portion of the legacy data storage facility 4732.
In embodiments, the streaming data collector 4710 may be configured with one or more automatic processors, algorithms, and/or other data methodologies to match up information captured by the one or more legacy instruments 4730 with a portion of data being provided by the one or more streaming devices 4740 from the one or more industrial machines 4712. Data from streaming devices 4740 may include a wider range of frequencies and resolutions than the sourced data of legacy instruments 4730 and, therefore, filtering and other such functions can be implemented to extract data from the streaming devices 4740 that corresponds to the sourced data of the legacy instruments 4730 in aspects such as frequency range, resolution, and the like. In embodiments, the configured streaming data collector 4710 may produce a plurality of streams of data, including a stream of data that may correspond to the stream of data from the streaming device 4740 and a separate stream of data that is compatible, in some aspects, with the legacy instrument sourced data and the infrastructure to ingest and automatically process it. Alternatively, the streaming data collector 4710 may output data in modes other than as a stream, such as batches, aggregations, summaries, and the like.
Configured streaming data collector 4710 may communicate with a stream storage facility 4764 for storing at least one of the data output from the streaming data collector 4710 and data extracted therefrom that may be compatible, in some aspects, with the sourced data of the legacy instruments 4730. A legacy compatible output of the configured streaming data collector 4710 may also be provided to a format adaptor facility 4748, 4760 that may configure, adapt, reformat and other adjustments to the legacy compatible data so that it can be stored in a legacy compatible storage facility 4762 so that legacy processing facilities 4744 may execute data processing methods on data in the legacy compatible storage facility 4762 and the like that are configured to process the sourced data of the legacy instruments 4730. In embodiments in which legacy compatible data is stored in the stream storage facility 4764, legacy processing facility 4744 may also automatically process this data after optionally being processed by format adaptor 4760. By arranging the data collection, streaming, processing, formatting, and storage elements to provide data in a format that is fully compatible with legacy instrument sourced data, transition from a legacy system can be simplified and the sourced data from legacy instruments can be easily compared to newly acquired data (with more content) without losing the legacy value of the sourced data from the legacy instruments 4730.
In embodiments, an industrial machine sensed data processing facility 4860 may execute a wide range of sensed data processing methods, some of which may be compatible with the data from legacy data sensors 4830 and may produce outputs that may meet legacy sensed data processing requirements. To facilitate use of a wide range of data processing capabilities of processing facility 4860, legacy and stream data may need to be aligned so that a compatible portion of stream data may be extracted for processing with legacy compatible methods and the like. In embodiments,
In embodiments, a second alignment methodology 4864 may involve aligning streaming data with data from a legacy data storage facility 4732. In embodiments, a third alignment methodology 4863 may involve aligning stored stream data from a stream storage facility 4884 with legacy data from the legacy data storage facility 4732. In each of the alignment methodologies 4862, 4864, 4863, alignment data may be determined by processing the legacy data to detect aspects such as resolution, duration, frequency range and the like. Alternatively, alignment may be performed by an alignment facility, such as facilities using alignment methodologies 4862, 4864, 4863 that may receive or may be configured with legacy data descriptive information such as legacy frequency range, duration, resolution, and the like.
In embodiments, an industrial machine sensing data processing facility 4868 may have access to legacy compatible methods and algorithms that may be stored in a legacy data methodology storage facility 4880. These methodologies, algorithms, or other data in the legacy algorithm storage facility 4762 may also be a source of alignment information that could be communicated by the industrial machine sensed data processing facility 4868 to the various alignment facilities having methodologies 4862, 4864, 4863. By having access to legacy compatible algorithms and methodologies, the data processing facility 4860 may facilitate processing legacy data, streamed data that is compatible with legacy data, or portions of streamed data that represent the legacy data to produce legacy compatible analytics 4630.
In embodiments, the data processing facility 4860 may execute a wide range of other sensed data processing methods, such as wavelet derivations and the like to produce streamed processed analytics 4631. In embodiments, the streaming data collector 102, 4510, 4610, 4710 (
Exemplary industrial machine deployments of the methods and systems described herein are now described. An industrial machine may be a gas compressor. In an example, a gas compressor may operate an oil pump on a very large turbo machine, such as a very large turbo machine that includes 10,000 HP motors. The oil pump may be a highly critical system as its failure could cause an entire plant to shut down. The gas compressor in this example may run four stages at a very high frequency, such as 36,000 RPM and may include tilt pad bearings that ride on an oil film. The oil pump in this example may have roller bearings, that if an anticipated failure is not being picked up by a user, the oil pump may stop running and the entire turbo machine would fail. Continuing with this example, the streaming data collector 102, 4510, 4610, 4710 may collect data related to vibrations, such as casing vibration and proximity probe vibration. Other bearing industrial machine examples may include generators, power plants, boiler feed pumps, fans, forced draft fans, induced draft fans and the like. The streaming data collector 102, 4510, 4610, 4710 for a bearings system used in the industrial gas industry may support predictive analysis on the motors, such as that performed by model-based expert systems, for example, using voltage, current and vibration as analysis metrics.
Another exemplary industrial machine deployment may be a motor and the streaming data collector 102, 4510, 4610, 4710 that may assist in the analysis of a motor by collecting voltage and current data on the motor, for example.
Yet another exemplary industrial machine deployment may include oil quality sensing. An industrial machine may conduct oil analysis and the streaming data collector 102, 4510, 4610, 4710 may assist in searching for fragments of metal in oil, for example.
The methods and systems described herein may also be used in combination with model-based systems. Model-based systems may integrate with proximity probes. Proximity probes may be used to sense problems with machinery and shut machinery down due to sensed problems. A model-based system integrated with proximity probes may measure a peak waveform and send a signal that shuts down machinery based on the peak waveform measurement.
Enterprises that operate industrial machines may operate in many diverse industries. These industries may include industries that operate manufacturing lines, provide computing infrastructure, support financial services, provide HVAC equipment and the like. These industries may be highly sensitive to lost operating time and the cost incurred due to lost operating time. HVAC equipment enterprises in particular may be concerned with data related to ultrasound, vibration, IR and the like and may get much more information about machine performance related to these metrics using the methods and systems of industrial machine sensed data streaming collection than from legacy systems.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for capturing a plurality of streams of sensed data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine; at least one of the streams containing a plurality of frequencies of data. The method may include identifying a subset of data in at least one of the plurality of streams that corresponds to data representing at least one predefined frequency. The at least one predefined frequency is represented by a set of data collected from alternate sensors deployed to monitor aspects of the industrial machine associated with the at least one moving part of the machine. The method may further include processing the identified data with a data processing facility that processes the identified data with data methodologies configured to be applied to the set of data collected from alternate sensors. Lastly the method may include storing the at least one of the streams of data, the identified subset of data, and a result of processing the identified data in an electronic data set.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for applying data captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the data captured with predefined lines of resolution covering a predefined frequency range to a frequency matching facility that identifies a subset of data streamed from other sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine, the streamed data comprising a plurality of lines of resolution and frequency ranges, the subset of data identified corresponding to the lines of resolution and predefined frequency range. This method may include storing the subset of data in an electronic data record in a format that corresponds to a format of the data captured with predefined lines of resolution; and signaling to a data processing facility the presence of the stored subset of data. This method may optionally include processing the subset of data with at least one of algorithms, methodologies, models, and pattern recognizers that corresponds to algorithms, methodologies, models, and pattern recognizers associated with processing the data captured with predefined lines of resolution covering a predefined frequency range.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for identifying a subset of streamed sensor data. The sensor data is captured from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. The subset of streamed sensor data is at predefined lines of resolution for a predefined frequency range. The method includes establishing a first logical route for communicating electronically between a first computing facility performing the identifying and a second computing facility. The identified subset of the streamed sensor data is communicated exclusively over the established first logical route when communicating the subset of streamed sensor data from the first facility to the second facility. This method may further include establishing a second logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that is not the identified subset. This method may further include establishing a third logical route for communicating electronically between the first computing facility and the second computing facility for at least one portion of the streamed sensor data that includes the identified subset and at least one other portion of the data not represented by the identified subset.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a first data sensing and processing system that captures first data from a first set of sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine, the first data covering a set of lines of resolution and a frequency range. This system may include a second data sensing and processing system that captures and streams a second set of data from a second set of sensors deployed to monitor aspects of the industrial machine associated with at least one moving part of the machine, the second data covering a plurality of lines of resolution that includes the set of lines of resolution and a plurality of frequencies that includes the frequency range. The system may enable (1) selecting a portion of the second data that corresponds to the set of lines of resolution and the frequency range of the first data; and (2) processing the selected portion of the second data with the first data sensing and processing system.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for automatically processing a portion of a stream of sensed data. The sensed data received from a first set of sensors is deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine in response to an electronic data structure that facilitates extracting a subset of the stream of sensed data that corresponds to a set of sensed data received from a second set of sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The set of sensed data is constrained to a frequency range. The stream of sensed data includes a range of frequencies that exceeds the frequency range of the set of sensed data. The processing comprising executing data methodologies on a portion of the stream of sensed data that is constrained to the frequency range of the set of sensed data. The data methodologies are configured to process the set of sensed data.
Methods and systems described herein for industrial machine sensor data streaming, collection, processing, and storage may be configured to operate and integrate with existing data collection, processing and storage systems and may include a method for receiving first data from sensors deployed to monitor aspects of an industrial machine associated with at least one moving part of the machine. This method may further include: (1) detecting at least one of a frequency range and lines of resolution represented by the first data; and (2) receiving a stream of data from sensors deployed to monitor the aspects of the industrial machine associated with the at least one moving part of the machine. The stream of data includes a plurality of frequency ranges and a plurality of lines of resolution that exceeds the frequency range and the lines of resolution represented by the first data; extracting a set of data from the stream of data that corresponds to at least one of the frequency range and the lines of resolution represented by the first data; and processing the extracted set of data with a data processing method that is configured to process data within the frequency range and within the lines of resolution of the first data.
The methods and systems disclosed herein may include, connect to, or be integrated with a data acquisition instrument and in the many embodiments,
In embodiments, the output signals from the sensors 5711, 5713, 5715 may be fed into instrument inputs 5020, 5022, 5024 of the DAQ instrument 5002 and may be configured with additional streaming capabilities 5028. By way of these many examples, the output signals from the sensors 5010, 5012, 5014, or more as applicable, may be conditioned as an analog signal before digitization with respect to at least scaling and filtering. The signals may then be digitized by an analog to digital converter 5030. The signals received from all relevant channels (i.e., one or more channels are switched on manually, by alarm, by route, and the like) may be simultaneously sampled at a predetermined rate sufficient to perform the maximum desired frequency analysis that may be adjusted and readjusted as needed or otherwise held constant to ensure compatibility or conformance with other relevant datasets. In embodiments, the signals are sampled for a relatively long time and gap-free as one continuous stream so as to enable further post-processing at lower sampling rates with sufficient individual sampling.
In embodiments, data may be streamed from a collection of points and then the next set of data may be collected from additional points according to a prescribed sequence, route, path, or the like. In many examples, the sensors 5010, 5012, 5014 or more may be moved to the next location according to the prescribed sequence, route, pre-arranged configurations, or the like. In certain examples, not all of the sensor 5711, 5713, 5715 may move and therefore some may remain fixed in place and used for detection of reference phase or the like.
In embodiments, a multiplex (mux) 5032 may be used to switch to the next collection of points, to a mixture of the two methods or collection patterns that may be combined, other predetermined routes, and the like. The multiplexer 5032 may be stackable so as to be laddered and effectively accept more channels than the DAQ instrument 5002 provides. In examples, the DAQ instrument 5002 may provide eight channels while the multiplexer 5032 may be stacked to supply 32 channels. Further variations are possible with one more multiplexers. In embodiments, the multiplexer 5032 may be fed into the DAQ instrument 5002 through an instrument input 5034. In embodiments, the DAQ instrument 5002 may include a controller 5038 that may take the form of an onboard controller, a PC, other connected devices, network based services, and combinations thereof.
In embodiments, the sequence and panel conditions used to govern the data collection process may be obtained from the multimedia probe (MMP) and probe control, sequence and analytical (PCSA) information store 5040. In embodiments, the PCSA information store 5040 may be onboard the DAQ instrument 5002. In embodiments, contents of the PCSA information store 5040 may be obtained through a cloud network facility, from other DAQ instruments, from other connected devices, from the machine being sensed, other relevant sources, and combinations thereof. In embodiments, the PCSA information store 5040 may include such items as the hierarchical structural relationships of the machine, e.g., a machine contains predetermined pieces of equipment, each of which may contain one or more shafts and each of those shafts may have multiple associated bearings. Each of those types of bearings may be monitored by specific types of transducers or probes, according to one or more specific prescribed sequences (paths, routes, and the like) and with one or more specific panel conditions that may be set on the one or more DAQ instruments 5002. By way of this example, the panel conditions may include hardware specific switch settings or other collection parameters. In many examples, collection parameters include but are not limited to a sampling rate, AC/DC coupling, voltage range and gain, integration, high and low pass filtering, anti-aliasing filtering, ICP™ transducers and other integrated-circuit piezoelectric transducers, 4-20 mA loop sensors, and the like. In embodiments, the PCSA information store 5040 may also include machinery specific features that may be important for proper analysis such as gear teeth for a gear, number blades in a pump impeller, number of motor rotor bars, bearing specific parameters necessary for calculating bearing frequencies, revolution per minutes information of all rotating elements and multiples of those RPM ranges, and the like. Information in the information store may also be used to extract stream data 5050 for permanent storage.
Based on directions from the DAQ API software 5052, digitized waveforms may be uploaded using DAQ driver services 5054 of a driver onboard the DAQ instrument 5002. In embodiments, data may then be fed into a raw data server 5058 which may store the stream data 5050 in a stream data repository 5060. In embodiments, this data storage area is typically meant for storage until the data is copied off of the DAQ instrument 5002 and verified. The DAQ API 5052 may also direct the local data control application 5062 to extract and process the recently obtained stream data 5050 and convert it to the same or lower sampling rates of sufficient length to effect one or more desired resolutions. By way of these examples, this data may be converted to spectra, averaged, and processed in a variety of ways and stored, at least temporarily, as extracted/processed (EP) data 5064. It will be appreciated in light of the disclosure that legacy data may require its own sampling rates and resolution to ensure compatibility and often this sampling rate may not be integer proportional to the acquired sampling rate. It will also be appreciated in light of the disclosure that this may be especially relevant for order-sampled data whose sampling frequency is related directly to an external frequency (typically the running speed of the machine or its local componentry) rather than the more-standard sampling rates employed by the internal crystals, clock functions, or the like of the DAQ instrument (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K, and so on).
In embodiments, the extract/process (EP) align module 5068 of the local data control application 5062 may be able to fractionally adjust the sampling rates to these non-integer ratio rates satisfying an important requirement for making data compatible with legacy systems. In embodiments, fractional rates may also be converted to integer ratio rates more readily because the length of the data to be processed may be adjustable. It will be appreciated in light of the disclosure that if the data was not streamed and just stored as spectra with the standard or predetermined Fmax, it may be impossible in certain situations to convert it retroactively and accurately to the order-sampled data. It will also be appreciated in light of the disclosure that internal identification issues may also need to be reconciled. In many examples, stream data may be converted to the proper sampling rate and resolution as described and stored (albeit temporarily) in an EP legacy data repository 5070 to ensure compatibility with legacy data.
To support legacy data identification issues, a user input module 5072 is shown in many embodiments should there be no automated process (whether partially or wholly) for identification translation. In such examples, one or more legacy systems (i.e., pre-existing data acquisition) may be characterized in that the data to be imported is in a fully standardized format such as a Mimosa™ format, and other similar formats. Moreover, sufficient indentation of the legacy data and/or the one or more machines from which the legacy data was produced may be required in the completion of an identification mapping table 5074 to associate and link a portion of the legacy data to a portion of the newly acquired streamed data 5050. In many examples, the end user and/or legacy vendor may be able to supply sufficient information to complete at least a portion of a functioning identification (ID) mapping table 5074 and therefore may provide the necessary database schema for the raw data of the legacy system to be used for comparison, analysis, and manipulation of newly streamed data 5050.
In embodiments, the local data control application 5062 may also direct streaming data as well as extracted/processed (EP) data to a cloud network facility 5080 via wired or wireless transmission. From the cloud network facility 5080 other devices may access, receive, and maintain data including the data from a master raw data server (MRDS) 5082. The movement, distribution, storage, and retrieval of data remote to the DAQ instrument 5002 may be coordinated by the cloud data management services (CDMS) 5084.
In embodiments, an expert analysis module 5100 may generate reports 5102 that may use machine or measurement point specific information from the PCSA information store 5040 to analyze the stream data 5050 using a stream data analyzer module 5104 and the local data control application 5062 with the extract/process (EP) align module 5068. In embodiments, the expert analysis module 5100 may generate new alarms or ingest alarm settings into an alarms module 5108 that is relevant to the stream data 5050. In embodiments, the stream data analyzer module 5104 may provide a manual or automated mechanism for extracting meaningful information from the stream data 5050 in a variety of plotting and report formats. In embodiments, a supervisory control of the expert analysis module 5100 is provided by the DAQ API 5052. In further examples, the expert analysis module 5100 may be supplied (wholly or partially) via the cloud network facility 5080. In many examples, the expert analysis module 5100 via the cloud may be used rather than a locally-deployed expert analysis module 5100 for various reasons such as using the most up-to-date software version, more processing capability, a bigger volume of historical data to reference, and so on. In many examples, it may be important that the expert analysis module 5100 be available when an internet connection cannot be established so having this redundancy may be crucial for seamless and time efficient operation. Toward that end, many of the modular software applications and databases available to the DAQ instrument 5002 where applicable may be implemented with system component redundancy to provide operational robustness to provide connectivity to cloud services when needed but also operate successfully in isolated scenarios where connectivity is not available and sometime not available purposefully to increase security and the like.
In embodiments, the DAQ instrument acquisition may require a real time operating system (RTOS) for the hardware especially for streamed gap-free data that is acquired by a PC. In some instances, the requirement for a RTOS may result in (or may require) expensive custom hardware and software capable of running such a system. In many embodiments, such expensive custom hardware and software may be avoided and an RTOS may be effectively and sufficiently implemented using a standard Windows™ operating systems or similar environments including the system interrupts in the procedural flow of a dedicated application included in such operating systems.
The methods and systems disclosed herein may include, connect to, or be integrated with one or more DAQ instruments and in the many embodiments,
In embodiments, the DAQ instrument 5002 may maintain a sufficiently large FIFO memory area 5152 that may buffer the incoming data so as to be not affected by operating system interrupts when acquiring data. It will be appreciated in light of the disclosure that the predetermined size of the FIFO memory area 5152 may be based on operating system interrupts that may include Windows system and application functions such as the writing of data to Disk or SSD, plotting, GUI interactions and standard Windows tasks, low-level driver tasks such as servicing the DAQ hardware and retrieving the data in bursts, and the like.
In embodiments, the computer, controller, connected device or the like that may be included in the DAQ instrument 5002 may be configured to acquire data from the one or more hardware devices over a USB port, firewire, ethernet, or the like. In embodiments, the DAQ driver services 5054 may be configured to have data delivered to it periodically so as to facilitate providing a channel specific FIFO memory buffer that may be configured to not miss data, i.e. it is gap-free. In embodiments, the DAQ driver services 5054 may be configured so as to maintain an even larger (than the device) channel specific FIFO memory area 5152 that it fills with new data obtained from the device. In embodiments, the DAQ driver services 5054 may be configured to employ a further process in that the raw data server 5058 may take data from the FIFO 5152 and may write it as a contiguous stream to non-volatile storage areas such as the stream data repository 5060 that may be configured as one or more disk drives, SSDs, or the like. In embodiments, the FIFO memory area 5152 may be configured to include a starting and stopping marker or pointer to mark where the latest most current stream was written. By way of these examples, a FIFO end marker 5254 may be configured to mark the end of the most current data until it reaches the end of the spooler and then wraps around constantly cycling around. In these examples, there is always one megabyte (or other configured capacities) of the most current data available in the FIFO memory area 5152 once the spooler fills up. It will be appreciated in light of the disclosure that further configurations of the FIFO memory area 5152 may be employed. In embodiments, the DAQ driver services 5054 may be configured to use the DAQ API 5052 to pipe the most recent data to a high-level application for processing, graphing and analysis purposes. In some examples, it is not required that this data be gap-free but even in these instances, it is helpful to identify and mark the gaps in the data. Moreover, these data updates may be configured to be frequent enough so that the user would perceive the data as live. In the many embodiments, the raw data is flushed to non-volatile storage without a gap at least for the prescribed amount of time and examples of the prescribed amount of time may be about thirty seconds to over four hours. It will be appreciated in light of the disclosure that many pieces of equipment and their components may contribute to the relative needed duration of the stream of gap-free data and those durations may be over four hours when relatively low speeds are present in large numbers, when non-periodic transient activity is occurring on a relatively long time frame, when duty cycle only permits operation in relevant ranges for restricted durations and the like.
With reference to
In many examples, any one of many transfer functions may be established between any two channels such as the two channels 5280, 5282 that may be shown on a screen 5284 shown on the display 5200, as shown in FIG. 27. The selection of the two channels 5280, 5282 on the screen 5284 may permit the user to depict the output of the transfer function on any of the screens including screen 5284 and screen 5204.
In embodiments,
In embodiments,
It will be appreciated in light of the disclosure that the sampling rates of vibration data of up to 100 kHz (or higher in some scenarios) may be utilized for non-vibration sensors as well. In doing so, it will further be appreciated in light of the disclosure that stream data in such durations at these sampling rates may uncover new patterns to be analyzed due in no small part that many of these types of sensors have not been utilized in this manner. It will also be appreciated in light of the disclosure that different sensors used in machinery condition monitoring may provide measurements more akin to static levels rather than fast-acting dynamic signals. In some cases, faster response time transducers may have to be used prior to achieving the faster sampling rates.
In many embodiments, sensors may have a relatively static output such as temperature, pressure, or flow but may still be analyzed with dynamic signal processing system and methodologies as disclosed herein. It will be appreciated in light of the disclosure that the time scale, in many examples, may be slowed down. In many examples, a collection of temperature readings collected approximately every minute for over two weeks may be analyzed for their variation solely or in collaboration or in fusion with other relevant sensors. By way of these examples, the direct current level or average level may be omitted from all the readings (e.g., by subtraction) and the resulting delta measurements may be processed (e.g., through a Fourier transform). From these examples, resulting spectral lines may correlate to specific machinery behavior or other symptoms present in industrial system processes. In further examples, other techniques include enveloping that may look for modulation, wavelets that may look for spectral patterns that last only for a short time (i.e., bursts), cross-channel analysis to look for correlations with other sensors including vibration, and the like.
In embodiments, there may be additional streaming hub servers such as the steaming hub server 5480 that may connect with other streaming sensors such as the streaming sensor 5490 that may include a DAQ instrument 5492, an endpoint node 5494, and the one or more analog sensors such as analog sensor 5498. In embodiments, the steaming hub server 5480 may also connect with other streaming sensors such as the streaming sensor 5500 that may include a DAQ instrument 5502, an endpoint node 5504, and the one or more analog sensors such as analog sensor 5508. In embodiments, the transmission may include averaged overall levels and in other examples may include dynamic signal sampled at a prescribed and/or fixed rate. In embodiments, the streaming sensors 5410, 5440, 5460, 5490, 5500 may be configured to acquire analog signals and then apply signal conditioning to those analog signals including coupling, averaging, integrating, differentiating, scaling, filtering of various kinds, and the like. The streaming sensors 5410, 5440, 5460, 5490, 5500 may be configured to digitize the analog signals at an acceptable rate and resolution (number of bits) and further processing the digitized signal when required. The streaming sensors 5410, 5440, 5460, 5490, 5500 may be configured to transmit the digitized signals at pre-determined, adjustable, and re-adjustable rates. In embodiments, the streaming sensors 5410, 5440, 5460, 5490, 5500 are configured to acquire, digitize, process, and transmit data at a sufficient effective rate so that a relatively consistent stream of data may be maintained for a suitable amount of time so that a large number of effective analyses may be shown to be possible. In the many embodiments, there would be no gaps in the data stream and the length of data should be relatively long, ideally for an unlimited amount of time, although practical considerations typically require ending the stream. It will be appreciated in light of the disclosure that this long duration data stream with effectively no gap in the stream is in contrast to the more commonly used burst collection where data is collected for a relatively short period of time (i.e., a short burst of collection), followed by a pause, and then perhaps another burst collection and so on. In the commonly used collections of data collected over noncontiguous bursts, data would be collected at a slow rate for low frequency analysis and high frequency for high frequency analysis. In many embodiments of the present disclosure, the streaming data is in contrast (i) being collected once, (ii) being collected at the highest useful and possible sampling rate, and (iii) being collected for a long enough time that low frequency analysis may be performed as well as high frequency. To facilitate the collection of the streaming data, enough storage memory must be available on the one or more streaming sensors such as the streaming sensors 5410, 5440, 5460, 5490, 5500 so that new data may be off-loaded externally to another system before the memory overflows. In embodiments, data in this memory would be stored into and accessed from in FIFO mode (First-In, First-Out). In these examples, the memory with a FIFO area may be a dual port so that the sensor controller may write to one part of it while the external system reads from a different part. In embodiments, data flow traffic may be managed by semaphore logic.
It will be appreciated in light of the disclosure that vibration transducers that are larger in mass will have a lower linear frequency response range because the natural resonance of the probe is inversely related to the square root of the mass and will be lowered. Toward that end, a resonant response is inherently non-linear and so a transducer with a lower natural frequency will have a narrower linear passband frequency response. It will also be appreciated in light of the disclosure that above the natural frequency the amplitude response of the sensor will taper off to negligible levels rendering it even more unusable. With that in mind, high frequency accelerometers, for this reason, tend to be quite small in mass of the order of half of a gram. It will also be appreciated in light of the disclosure that adding the required signal processing and digitizing electronics required for streaming may, in certain situations, render the sensors incapable in many instances of measuring high-frequency activity.
In embodiments, streaming hubs such as the streaming hubs 5420, 5480 may effectively move the electronics required for streaming to an external hub via cable. It will be appreciated in light of the disclosure that the streaming hubs may be located virtually next to the streaming sensors or up to a distance supported by the electronic driving capability of the hub. In instances where an internet cache protocol (ICP) is used, the distance supported by the electronic driving capability of the hub would be anywhere from 100 to 1000 feet (30.5 to 305 meters) based on desired frequency response, cable capacitance and the like. In embodiments, the streaming hubs may be positioned in a location convenient for receiving power as well as connecting to a network (be it LAN or WAN). In embodiments, other power options would include solar, thermal as well as energy harvesting. Transfer between the streaming sensors and any external systems may be wireless or wired and may include such standard communication technologies as 802.11 and 900 MHz wireless systems, Ethernet, USB, firewire and so on.
With reference to
With further reference to
In embodiments, the MRDS 5700 may include a stream data analyzer module 5710 with an extract and process alignment module. The analyzer module 5710 may be shown to be a more robust data analyzer and extractor than may be typically found on portable streaming DAQ instruments although it may be deployed on the DAQ instrument 5002 as well. In embodiments, the analyzer module 5710 takes streaming data and instantiates it at a specific sampling rate and resolution similar to the local data control module 5062 on the DAQ instrument 5002. The specific sampling rate and resolution of the analyzer module 5710 may be based on either user input 5712 or automated extractions from a multimedia probe (MMP) and the probe control, sequence and analytical (PCSA) information store 5714 and/or an identification mapping table 5718, which may require the user input 5712 if there is incomplete information regarding various forms of legacy data similar to as was detailed with the DAQ instrument 5002. In embodiments, legacy data may be processed with the analyzer module 5710 and may be stored in one or more temporary holding areas such as a new legacy data repository 5722. One or more temporary areas may be configured to hold data until it is copied to an archive and verified. The analyzer 5710 module may also facilitate in-depth analysis by providing many varying types of signal processing tools including but not limited to filtering, Fourier transforms, weighting, resampling, envelope demodulation, wavelets, two-channel analysis, and the like. From this analysis, many different types of plots and mini-reports may be generated from a reports and plots module 5724. In embodiments, data is sent to the processing, analysis, reports, and archiving (PARA) server 5730 upon user initiation or in an automated fashion especially for on-line systems.
In embodiments (
In embodiments, portable connected devices 5850 such a tablet 5852 and a smart phone 5854 may connect the CDMS 5832 using web APIs 5860 and 5862, respectively, as depicted in
In embodiments, the CDMS 5832 is depicted in greater detail in
In embodiments, a relational database server (RDS) 5930 may be used to access all of the information from a multimedia probe (MMP) and probe control, sequence and analytical (PCSA) information store 5932. As with the PARA server 5800 (
In embodiments, the streaming data may be linked with the RDS 5930 and the MMP and PCSA information store 5932 using a technical data management streaming (TDMS) file format. In embodiments, the information store 5932 may include tables for recording at least portions of all measurement events. By way of these examples, a measurement event may be any single data capture, a stream, a snapshot, an averaged level, or an overall level. Each of the measurement events in addition to point identification information may also have a date and time stamp. In embodiments, a link may be made between the streaming data, the measurement event, and the tables in the information store 5932 using the TDMS format. By way of these examples, the link may be created by storing a unique measurement point identification codes with a file structure having the TDMS format by including and assigning TDMS properties. In embodiments, a file with the TDMS format may allow for three levels of hierarchy. By way of these examples, the three levels of hierarchy may be root, group, and channel. It will be appreciated in light of the disclosure that the Mimosa™ database schema may be, in theory, unlimited. With that said, there are advantages to limited TDMS hierarchies. In the many examples, the following properties may be proposed for adding to the TDMS Stream structure while using a Mimosa Compatible database schema.
Root Level:
Global ID 1: Text String (This could be a unique ID obtained from the web.)
Global ID 2: Text String (This could be an additional ID obtained from the web.)
Company Name: Text String
Company ID: Text String
Company Segment ID: 4-byte Integer
Company Segment ID: 4-byte Integer
Site Name: Text String
Site Segment ID: 4-byte Integer
Site Asset ID: 4-byte Integer
Route Name: Text String
Version Number: Text String
Group Level:
Section 1 Name: Text String
Section 1 Segment ID: 4-byte Integer
Section 1 Asset ID: 4-byte Integer
Section 2 Name: Text String
Section 2 Segment ID: 4-byte Integer
Section 2 Asset ID: 4-byte Integer
Machine Name: Text String
Machine Segment ID: 4-byte Integer
Machine Asset ID: 4-byte Integer
Equipment Name: Text String
Equipment Segment ID: 4-byte Integer
Equipment Asset ID: 4-byte Integer
Shaft Name: Text String
Shaft Segment ID: 4-byte Integer
Shaft Asset ID: 4-byte Integer
Bearing Name: Text String
Bearing Segment ID: 4-byte Integer
Bearing Asset ID: 4-byte Integer
Probe Name: Text String
Probe Segment ID: 4-byte Integer
Probe Asset ID: 4-byte Integer
Channel Level:
Channel #: 4-byte Integer
Direction: 4-byte Integer (in certain examples may be text)
Data Type: 4-byte Integer
Reserved Name 1: Text String
Reserved Segment ID 1: 4-byte Integer
Reserved Name 2: Text String
Reserved Segment ID 2: 4-byte Integer
Reserved Name 3: Text String
Reserved Segment ID 3: 4-byte Integer
In embodiments, the file with the TDMS format may automatically use property or asset information and may make an index file out of the specific property and asset information to facilitate database searches. It will be appreciated in light of the disclosure that the TDMS format may offer a compromise for storing voluminous streams of data because it may be optimized for storing binary streams of data but may also include some minimal database structure making many standard SQL operations feasible. It will also be appreciated in light of the disclosure that the TDMS format and functionality discussed herein may not be as efficient as a full-fledged SQL relational database, the TDMS format, however, may take advantages of both worlds in that it may balance between the class or format of writing and storing large streams of binary data efficiently and the class or format of a fully relational database which facilitates searching, sorting and data retrieval. In embodiments, an optimum solution may be found such that metadata required for analytical purposes and extracting prescribed lists with panel conditions for stream collection may be stored in the RDS 5930 by establishing a link between the two database methodologies. By way of these examples, relatively large analog data streams may be stored predominantly as binary storage in the raw data stream archive 5942 for rapid stream loading but with inherent relational SQL type hooks, formats, conventions, or the like. The files with the TDMS format may also be configured to incorporate DIAdem™ reporting capability of LabVIEW™ software so as to provide a further mechanism to facilitate conveniently and rapidly accessing the analog or the streaming data.
The methods and systems disclosed herein may include, connect to, or be integrated with a virtual data acquisition instrument and in the many embodiments,
In embodiments, storage of streaming data, as well as the extraction and processing of streaming data into extract and process data, may be handled primarily by the DAQ driver services 6012 under the direction of the DAQ Web API 6010. In embodiments, the output from sensors of various types including vibration, temperature, pressure, ultrasound and so on may be fed into the instrument inputs of the DAQ device 6004. In embodiments, the signals from the output sensors may be signal conditioned with respect to scaling and filtering and digitized with an analog to digital converter. In embodiments, the signals from the output sensors may be signals from all relevant channels simultaneously sampled at a rate sufficient to perform the maximum desired frequency analysis. In embodiments, the signals from the output sensors may be sampled for a relatively long time, gap-free as one continuous stream so as to enable a wide array of further post-processing at lower sampling rates with sufficient samples. In further examples, streaming frequency may be adjusted (and readjusted) to record streaming data at non-evenly spaced recording. For temperature data, pressure data, and other similar data that may be relatively slow, varying delta times between samples may further improve quality of the data. By way of the above examples, data may be streamed from a collection of points and then the next set of data may be collected from additional points according to a prescribed sequence, route, path, or the like. In the many examples, the portable sensors may be moved to the next location according to the prescribed sequence but not necessarily all of them as some may be used for reference phase or otherwise. In further examples, a multiplexer 6020 may be used to switch to the next collection of points or a mixture of the two methods may be combined.
In embodiments, the sequence and panel conditions that may be used to govern the data collection process using the virtual DAQ instrument 6000 may be obtained from the MMP PCSA information store 6022. The MMP PCSA information store 6022 may include such items as the hierarchical structural relationships of the machine, e.g., a machine contains pieces of equipment in which each piece of equipment contains shafts and each shaft is associated with bearings, which may be monitored by specific types of transducers or probes according to a specific prescribed sequence (routes, path, etc.) with specific panel conditions. By way of these examples, the panel conditions may include hardware specific switch settings or other collection parameters such as sampling rate, AC/DC coupling, voltage range and gain, integration, high and low pass filtering, anti-aliasing filtering, ICP™ transducers and other integrated-circuit piezoelectric transducers, 4-20 mA loop sensors, and the like. The information store 6022 includes other information that may be stored in what would be machinery specific features that would be important for proper analysis including the number of gear teeth for a gear, the number of blades in a pump impeller, the number of motor rotor bars, bearing specific parameters necessary for calculating bearing frequencies, 1× rotating speed (e.g., RPMs) of all rotating elements, and the like.
Upon direction of the DAQ Web API 6010 software, digitized waveforms may be uploaded using the DAQ driver services 6012 of the virtual DAQ instrument 6000. In embodiments, data may then be fed into an RLN data and control server 6030 that may store the stream data into a network stream data repository 6032. Unlike the DAQ instrument 5002, the server 6030 may run from within the DAQ driver module 6002. It will be appreciated in light of the disclosure that a separate application may require drivers for running in the native operating system and for this instrument only the instrument driver may run natively. In many examples, all other applications may be configured to be browser based. As such, a relevant network variable may be very similar to a LabVIEW™ shared or network stream variable which may be designed to be accessed over one or more networks or via web applications.
In embodiments, the DAQ Web API 6010 may also direct the local data control application 6034 to extract and process the recently obtained streaming data and, in turn, convert it to the same or lower sampling rates of sufficient length to provide the desired resolution. This data may be converted to spectra, then averaged and processed in a variety of ways and stored as extracted/processed (EP) data 6040. The EP data repository 6040 but this repository may, in certain embodiments, only be meant for temporary storage. It will be appreciated in light of the disclosure that legacy data may require its own sampling rates and resolution and often this sampling rate may not be integer proportional to the acquired sampling rate especially for order-sampled data whose sampling frequency is related directly to an external frequency, which is typically the running speed of the machine or its internal componentry, rather than the more-standard sampling rates produced by the internal crystals, clock functions, and the like of the (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K and so on) of the DAQ instrument 5002, 6000. In embodiments, the EP (extract/process) align component of the local data control application 6034 is able to fractionally adjust the sampling rate to the non-integer ratio rates that may be more applicable to legacy data sets and therefore driving compatibility with legacy systems. In embodiments, the fractional rates may be converted to integer ratio rates more readily because the length of the data to be processed (or at least that portion of the greater stream of data) is adjustable because of the depth and content of the original acquired streaming data by the DAQ instrument 5002, 6000. It will be appreciated in light of the disclosure that if the data was not streamed and just stored as traditional snap-shots of spectra with the standard values of Fmax, it may very well be impossible to convert retroactively and accurately the acquired data to the order-sampled data. In embodiments, the stream data may be converted, especially for legacy data purposes, to the proper sampling rate and resolution as described and stored in the EP legacy data repository 6042. To support legacy data identification scenarios, a user input 6044 may be included should there be no automated process for identification translation. In embodiments, one such automated process for identification translation may include importation of data from a legacy system that may contain fully standardized format such as Mimosa™ format and sufficient identification information to complete an ID Mapping Table 6048. In further examples, the end user, a legacy data vendor, a legacy data storage facility, or the like may be able to supply enough info to complete (or sufficiently complete) relevant portions of the ID Mapping Table 6048 to provide, in turn, the database schema for the raw data of the legacy system so it may be readily ingested, saved, and use for analytics in the current systems disclosed herein.
The virtual DAQ Instrument 6000 may also include an expert analysis module 6052. In embodiments, the expert analysis module 6052 may be a web application or other suitable modules that may generate reports 4916 that may use machine or measurement point specific information from the MMP PCSA information store 6022 to analyze stream data 6058 using the stream data analyzer module 6050. In embodiments, supervisory control of the expert analysis module 6052 may be provided by the DAQ Web API 6010. In embodiments, the expert analysis may also be supplied (or supplemented) via the expert system module 5940 that may be resident on one or more cloud network facilities that are accessible via the CDMS 5832. In many examples, expert analysis via the cloud may be preferred over local systems such the expert analysis module 6052 for various reasons such as the availability and use of the most up-to-date software version, more processing capability, a bigger volume of historical data to reference and the like. It will be appreciated in light of the disclosure that it may be important to offer expert analysis when an internet connection cannot be established so as to provide a redundancy, when needed, for seamless and time efficient operation. In embodiments, this redundancy may be extended to all of the discussed modular software applications and databases where applicable so each module discussed herein may be configured to provide redundancy to continue operation in the absence of an internet connection.
In embodiments, the MDCA 7008 may be configured to provide automated as well as user-directed analyses of the raw data that may include tracking and annotating specific occurrence and in doing so, noting where reports may be generated and alarms may be noted. In embodiments, the SCI 7010 may be an application configured to provide remote control of the system from the cloud as well as the ability to generate status and alarms. In embodiments, the SCI 7010 may be configured to connect to, interface with, or be integrated into a supervisory control and data acquisition (SCADA) control system. In embodiments, the SCI 7010 may be configured as a LabVIEW™ application that may provide remote control and status alerts that may be provided to any remote device that may connect to one or more of the cloud network facilities 5870.
In embodiments, the equipment that is being monitored may include RFID tags that may provide vital machinery analysis background information. The RFID tags may be associated with the entire machine or associated with the individual componentry and may be substituted when certain parts of the machine are replaced, repair, or rebuilt. The RFID tags may provide permanent information relevant to the lifetime of the unit or may also be re-flashed to update with at least portion of new information. In many embodiments, the DAQ instruments 5002 disclosed herein may interrogate the one or RFID chips to learn of the machine, its componentry, its service history, and the hierarchical structure of how everything is connected including drive diagrams, wire diagrams, and hydraulic layouts. In embodiments, some of the information that may be retrieved from the RFID tags includes manufacturer, machinery type, model, serial number, model number, manufacturing date, installation date, lots numbers, and the like. By way of these examples, machinery type may include the use of a Mimosa™ format table including information about one or more of the following motors, gearboxes, fans, and compressors. The machinery type may also include the number of bearings, their type, their positioning, and their identification numbers. The information relevant to the one or more fans includes fan type, number of blades, number of vanes, and number belts. It will be appreciated in light of the disclosure that other machines and their componentry may be similarly arranged hierarchically with relevant information all of which may be available through interrogation of one or more RFID chips associated with the one or more machines.
In embodiments, data collection in an industrial environment may include routing analog signals from a plurality of sources, such as analog sensors, to a plurality of analog signal processing circuits. Routing of analog signals may be accomplished by an analog crosspoint switch that may route any of a plurality of analog input signals to any of a plurality of outputs, such as to analog and/or digital outputs. Routing of inputs to outputs in an analog signal crosspoint switch in an industrial environment may be configurable, such by an electronic signal to which a switch portion of the analog crosspoint switch is responsive.
In embodiments, the analog crosspoint switch may receive analog signals from a plurality of analog signal sources in the industrial environment. Analog signal sources may include sensors that produce an analog signal. Sensors that produce an analog signal that may be switched by the analog crosspoint switch may include sensors that detect a condition and convert it to analog signal that may be representative of the condition, such as converting a condition to a corresponding voltage. Exemplary conditions that may be represented by a variable voltage may include temperature, friction, sound, light, torque, revolutions-per-minute, mechanical resistance, pressure, flow rate, and the like, including any of the conditions represented by inputs sources and sensors disclosed throughout this disclosure and the documents incorporated herein by reference. Other forms of analog signal may include electrical signals, such as variable voltage, variable current, variable resistance, and the like.
In embodiments, the analog crosspoint switch may preserve one or more aspects of an analog signal being input to it in an industrial environment. Analog circuits integrated into the switch may provide buffered outputs. The analog circuits of the analog crosspoint switch may follow an input signal, such as an input voltage to produce a buffered representation on an output. This may alternatively be accomplished by relays (mechanical, solid state, and the like) that allow an analog voltage or current present on an input to propagate to a selected output of the analog switch.
In embodiments, an analog crosspoint switch in an industrial environment may be configured to switch any of a plurality of analog inputs to any of a plurality of analog outputs. An analog crosspoint switch may be dynamically configurable so that changes to the configuration causes a change in the mapping of inputs to outputs. A configuration change may apply to one or more mappings so that a change in mapping may result in one or more of the outputs being mapped to different input than before the configuration change.
In embodiments, the analog crosspoint switch may have more inputs than outputs, so that only a subset of inputs can be routed to outputs concurrently. In other embodiments, the analog crosspoint switch may have more outputs than inputs, so that either a single input may be made available currently on multiple outputs, or at least one output may not be mapped to any input.
In embodiments, an analog crosspoint switch in an industrial environment may be configured to switch any of a plurality of analog inputs to any of a plurality of digital outputs. To accomplish conversion from analog inputs to digital outputs, an analog to digital converter circuit may be configured on each input, each output, or at intermediate points between the input(s) and output(s) of the analog crosspoint switch. Benefits of including digitization of analog signals in an analog crosspoint switch that may be located close to analog signal sources may include reducing signal transport costs and complexity that digital signal communication has over analog, reducing energy consumption, facilitating detection and regulation of aberrant conditions before they propagate throughout an industrial environment, and the like. Capturing analog signals close to their source may also facilitate improved signal routing management that is more tolerant of real world effects such as requiring that multiple signals be routed simultaneously. In this example, a portion of the signals can be captured (and stored) locally while another portion can be transferred through the data collection network. Once the data collection network has available bandwidth, the locally stored signals can be delivered, such as with a time stamp indicating the time at which the data was collected. This technique may be useful for applications that have concurrent demand for data collection channels that exceeds the number of channels available. Sampling control may also be based on an indication of data worth sampling. As an example, a signal source, such as a sensor in an industrial environment may provide a data valid signal that transmits an indication of when data from the sensor is available.
In embodiments, mapping inputs of the analog crosspoint switch to outputs may be based on a signal route plan for a portion of the industrial environment that may be presented to the crosspoint switch. The signal route plan may be used in a method of data collection in the industrial environment that may include routing a plurality of analog signals along a plurality of analog signal paths. The method may include connecting the plurality of analog signals individually to inputs of the analog crosspoint switch that may be configured with a route plan. The crosspoint switch may, responsively to the configured route plan, route a portion of the plurality of analog signals to a portion of the plurality of analog signal paths.
In embodiments, the analog crosspoint switch may include at least one high current output drive circuit that may be suitable for routing the analog signal along a path that the requires high current. In embodiments, the analog crosspoint switch may include at least one voltage-limited input that may facilitate protecting the analog crosspoint switch from damage due to excessive analog input signal voltage. In embodiments, the analog crosspoint switch may include at least one current limited input that may facilitate protecting the analog crosspoint switch from damage due to excessive analog input current. The analog crosspoint switch may comprise a plurality of interconnected relays that may facilitate routing the input(s) to the output(s) with little or no substantive signal loss.
In embodiments, an analog crosspoint switch may include processing functionality, such as signal processing and the like (e.g., a programmed processor, special purpose processor, a digital signal processor, and the like) that may detect one or more analog input signal conditions. In response to such detection, one or more actions may be performed, such as setting an alarm, sending an alarm signal to another device in the industrial environment, changing the crosspoint switch configuration, disabling one or more outputs, powering on/off a portion of the switch, change a state of an output, such as a general purpose digital or analog output, and the like. In embodiments, the switch may be configured to process inputs for producing a signal on one or more of the outputs. The inputs to use, processing algorithm for the inputs, condition for producing the signal, output to use, and the like may be configured in a data collection template.
In embodiments, an analog crosspoint switch may comprise greater than 32 inputs and greater than 32 outputs. A plurality of analog crosspoint switches may be configured so that even though each switch offers less than 32 inputs and 32 outputs the plurality of analog crosspoint switches may be configured to facilitate switching any of 32 inputs to any of 32 outputs spread across the plurality of crosspoint switches.
In embodiments, an analog crosspoint switch suitable for use in an industrial environment may comprise four or fewer inputs and four or fewer outputs. Each output may be configurable to produce an analog output that corresponds to the mapped analog input, or it may be configured to produce a digital representation of the corresponding mapped input.
In embodiments, an analog crosspoint switch for use in an industrial environment may be configured with circuits that facilitate replicating at least a portion of attributes of the input signal, such as current, voltage range, offset, frequency, duty cycle, ramp rate, and the like while buffering (e.g., isolating) the input signal from the output signal. Alternatively, an analog crosspoint switch may be configured with unbuffered inputs/outputs, thereby effectively producing a bi-directional based crosspoint switch).
In embodiments, an analog crosspoint switch for use in an industrial environment may include protected inputs that may be protected from damaging conditions, such as through use of signal conditioning circuits. Protected inputs may prevent damage to the switch and to downstream devices that the switch outputs connect to. As an example, inputs to such an analog crosspoint switch may include voltage clipping circuits that prevent a voltage of an input signal from exceeding an input protection threshold. An active voltage adjustment circuit may scale an input signal by reducing it uniformly so that a maximum voltage present on the input does not exceed a safe threshold value. As another example, inputs to such an analog crosspoint switch may include current shunting circuits that cause current beyond a maximum input protection current threshold to be diverted through protection circuits rather than enter the switch. Analog switch inputs may be protected from electrostatic discharge and/or lightning strikes. Other signal conditioning functions that may be applied to inputs to an analog crosspoint switch may include voltage scaling circuitry that attempts to facilitate distinguishing between valid input signals and low voltage noise that may be present on the input. However, in embodiments, inputs to the analog crosspoint switch may be unbuffered and/or unprotected to make the least impact on the signal. Signals such as alarm signals, or signals that cannot readily tolerate protection schemes, such as those schemes described above herein may be connected to unbuffered inputs of the analog crosspoint switch.
In embodiments, an analog crosspoint switch may be configured with circuitry, logic, and/or processing elements that may facilitate input signal alarm monitoring. Such an analog crosspoint switch may detect inputs meeting alarm conditions and in response thereto, switch inputs, switch mapping of inputs to outputs, disable inputs, disable outputs, issue an alarm signal, activate/deactivate a general-purpose output, and the like.
In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to selectively power up or down portions of the analog crosspoint switch or circuitry associated with the analog crosspoint switch, such as input protection devices, input conditioning devices, switch control devices and the like. Portions of the analog crosspoint switch that may be powered on/off may include outputs, inputs, sections of the switch and the like. In an example, an analog crosspoint switch may include a modular structure that may separate portions of the switch into independently powered sections. Based on conditions, such as an input signal meeting a criterion or a configuration value being presented to the analog crosspoint switch, one or more modular sections may be powered on/off.
In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to perform signal processing including, without limitation providing a voltage reference for detecting an input crossing the voltage reference (e.g., zero volts for detecting zero-crossing signals), a phase-lock loop to facilitate capturing slow frequency signals (e.g., low-speed revolution-per-minute signals and detecting their corresponding phase), deriving input signal phase relative to other inputs, deriving input signal phase relative to a reference (e.g., a reference clock), deriving input signal phase relative to detected alarm input conditions and the like. Other signal processing functions of such an analog crosspoint switch may include oversampling of inputs for delta-sigma A/D, to produce lower sampling rate outputs, to minimize AA filter requirements and the like. Such an analog crosspoint switch may support long block sampling at a constant sampling rate even as inputs are switched, which may facilitate input signal rate independence and reduce complexity of sampling scheme(s). A constant sampling rate may be selected from a plurality of rates that may be produced by a circuit, such as a clock divider circuit that may make available a plurality of components of a reference clock.
In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to support implementing data collection/data routing templates in the industrial environment. The analog crosspoint switch may implement a data collection/data routing template based on conditions in the industrial environment that it may detect or derive, such as an input signal meeting one or more criteria (e.g., transition of a signal from a first condition to a second, lack of transition of an input signal within a predefined time interface (e.g., inactive input) and the like).
In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to be configured from a portion of a data collection template. Configuration may be done automatically (e.g., without needing human intervention to perform a configuration action or change in configuration), such as based on a time parameter in the template and the like. Configuration may be done remotely, e.g., by sending a signal from a remote location that is detectable by a switch configuration feature of the analog crosspoint switch. Configuration may be done dynamically, such as based on a condition that is detectable by a configuration feature of the analog crosspoint switch (e.g., a timer, an input condition, an output condition, and the like). In embodiments, information for configuring an analog crosspoint switch may be provided in a stream, as a set of control lines, as a data file, as an indexed data set, and the like. In embodiments, configuration information in a data collection template for the switch may include a list of each input and a corresponding output, a list of each output function (active, inactive, analog, digital and the like), a condition for updating the configuration (e.g., an input signal meeting a condition, a trigger signal, a time (relative to another time/event/state, or absolute), a duration of the configuration, and the like. In embodiments configuration of the switch may be input signal protocol aware so that switching from a first input to a second input for a given output may occur based on the protocol. In an example, a configuration change may be initiated with the switch to switch from a first video signal to a second video signal. The configuration circuitry may detect the protocol of the input signal and switch to the second video signal during a synchronization phase of the video signal, such as during horizontal or vertical refresh. In other examples, switching may occur when one or more of the inputs are at zero volts. This may occur for a sinusoidal signal that transitions from below zero volts to above zero volts.
In embodiments, a system for collecting data in an industrial environment may include an analog crosspoint switch that may be adapted to provide digital outputs by converting analog signals input to the switch into digital outputs. Converting may occur after switching the analog inputs based on a data collection template and the like. In embodiments, a portion of the switch outputs may be digital, and a portion may be analog. Each output, or groups thereof, may be configurable as analog or digital, such as based on analog crosspoint switch output configuration information included in or derived from a data collection template. Circuitry in the analog crosspoint switch may sense an input signal voltage range and intelligently configure an analog to digital conversion function accordingly. As an example, a first input may have a voltage range of 12 volts and a second input may have a voltage range of 24 volts. Analog to digital converter circuits for these inputs may be adjusted so that the full range of the digital value (e.g., 256 levels for an 8-bit signal) will map substantially linearly to 12 volts for the first input and 24 volts for the second input.
In embodiments, an analog crosspoint switch may automatically configure input circuitry based on characteristics of a connected analog signal. Examples of circuitry configuration may include setting a maximum voltage, a threshold based on a sensed maximum threshold, a voltage range above and/or below a ground reference, an offset reference, and the like. The analog crosspoint switch may also adapt inputs to support voltage signals, current signals, and the like. The analog crosspoint switch may detect a protocol of an input signal, such as a video signal protocol, audio signal protocol, digital signal protocol, protocol based on input signal frequency characteristics, and the like. Other aspects of inputs of the analog crosspoint switch that may be adapted based on the incoming signal may include a duration of sampling of the signal, and comparator or differential type signals, and the like.
In embodiments, an analog crosspoint switch may be configured with functionality to counteract input signal drift and/or leakage that may occur when an analog signal is passed through it over a long period of time without changing value (e.g., a constant voltage). Techniques may include voltage boost, current injection, periodic zero referencing (e.g., temporarily connecting the input to a reference signal, such as ground, applying a high resistance pathway to the ground reference, and the like).
In embodiments, a system for data collection in an industrial environment may include an analog crosspoint switch deployed in an assembly line comprising conveyers and/or lifters. A power roller conveyor system includes many rollers that deliver product along a path. There may be many points along the path that may be monitored for proper operation of the rollers, load being placed on the rollers, accumulation of products, and the like. A power roller conveyor system may also facilitate moving product through longer distances and therefore may have a large number of products in transport at once. A system for data collection in such an assembly environment may include sensors that detect a wide range of conditions as well as at a large number of positions along the transport path. As a product progresses down the path, some sensors may be active and others, such as those that the product has passed maybe inactive. A data collection system may use an analog crosspoint switch to select only those sensors that are currently or anticipated to be active by switching from inputs that connect to inactive sensors to those that connect to active sensors and thereby provide the most useful sensor signals to data detection and/or collection and/or processing facilities. In embodiments, the analog crosspoint switch may be configured by a conveyor control system that monitors product activity and instructs the analog crosspoint switch to direct different inputs to specific outputs based on a control program or data collection template associated with the assembly environment.
In embodiments, a system for data collection in an industrial environment may include an analog crosspoint switch deployed in a factory comprising use of fans as industrial components. In embodiments, fans in a factory setting may provide a range of functions including drying, exhaust management, clean air flow and the like. In an installation of a large number of fans, monitoring fan rotational speed, torque, and the like may be beneficial to detect an early indication of a potential problem with air flow being produced by the fans. However, concurrently monitoring each of these elements for a large number of fans may be inefficient. Therefore, sensors, such as tachometers, torque meters, and the like may be disposed at each fan and their analog output signal(s) may be provided to an analog crosspoint switch. With a limited number of outputs, or at least a limited number of systems that can process the sensor data, the analog crosspoint switch may be used to select among the many sensors and pass along a subset of the available sensor signals to data collection, monitoring, and processing systems. In an example, sensor signals from sensors disposed at a group of fans may be selected to be switched onto crosspoint switch outputs. Upon satisfactory collection and/or processing of the sensor signals for this group of fans, the analog crosspoint switch may be reconfigured to switch signals from another group of fans to be processed.
In embodiments, a system for data collection in an industrial environment may include an analog crosspoint switch deployed as an industrial component in a turbine-based power system. Monitoring for vibration in turbine systems, such as hydro-power systems, has been demonstrated to provide advantages in reduction in down time. However, with a large number of areas to monitor for vibration, particularly for on-line vibration monitoring, including relative shaft vibration, bearings absolute vibration, turbine cover vibration, thrust bearing axial vibration, stator core vibrations, stator bar vibrations, stator end winding vibrations, and the like, it may be beneficial to select among this list over time, such as taking samples from sensors for each of these types of vibration a few at a time. A data collection system that includes an analog crosspoint switch may provide this capability by connecting each vibration sensor to separate inputs of the analog crosspoint switch and configuring the switch to output a subset of its inputs. A vibration data processing system, such as a computer, may determine which sensors to pass through the analog crosspoint switch and configure an algorithm to perform the vibration analysis accordingly. As an example, sensors for capturing turbine cover vibration may be selected in the analog crosspoint switch to be passed on to a system that is configured with an algorithm to determine turbine cover vibration from the sensor signals. Upon completion of determining turbine cover vibration, the crosspoint switch may be configured to pass along thrust bearing axial vibration sensor signals and a corresponding vibration analysis algorithm may be applied to the data. In this way, each type of vibration may be analyzed by a single processing system that works cooperatively with an analog crosspoint switch to pass specific sensor signals for processing.
Referring to
1. A system for data collection in an industrial environment comprising;
a plurality of analog signal sources that each connect to at least one input of an analog crosspoint switch comprising a plurality of inputs and a plurality of outputs;
wherein the analog crosspoint switch is configurable to switch a portion of the input signal sources to a plurality of the outputs.
2. The system of clause 1, wherein the analog crosspoint switch further comprises an analog to digital converter that converts a portion of analog signals input to the crosspoint switch into representative digital signals
3. The system of clause 1, wherein a first portion signals at the plurality of outputs comprises analog output signals and a second portion of signals at the plurality of outputs comprises digital output signals.
4. The system of clause 1, wherein the analog crosspoint switch is adapted to detect one or more analog input signal conditions.
5. The system of clauses 1-4 wherein the one or more analog input signal conditions comprise a voltage range of the signal, and wherein the analog crosspoint switch responsively adjusts input circuitry to comply with detected voltage range.
6. A system of data collection in an industrial environment comprising:
a plurality of industrial sensors that produce analog signals representative of a condition of an industrial machine in the environment being sensed by the plurality of industrial sensors; and
a crosspoint switch that receives the analog signals and routes the analog signals to separate analog outputs of the crosspoint switch based on a signal route plan presented to the crosspoint switch.
7. The system of clauses 1-6, wherein the analog crosspoint switch further comprises an analog to digital converter that converts a portion of analog signals input to the crosspoint switch into representative digital signals
8. The system of clauses 1-6, wherein a first portion of signals at the plurality of outputs comprises analog output signals and a second portion of signals at the plurality of outputs comprises digital output signals.
9. The system of clauses 1-6, wherein the analog crosspoint switch is adapted to detect one or more analog input signal conditions.
10. The system of clauses 1-9 wherein the one or more analog input signal conditions comprise a voltage range of the signal, and wherein the analog crosspoint switch responsively adjusts input circuitry to comply with detected voltage range.
11. A method of data collection in an industrial environment comprising routing a plurality of analog signals along a plurality of analog signal paths by:
connecting the plurality of analog signals individually to inputs of an analog crosspoint switch;
configuring the analog crosspoint switch with data routing information from a data collection template for the industrial environment; and
routing, with the configured analog crosspoint switch a portion of the plurality of analog signals to a portion the plurality of analog signal paths.
12. The method of clauses 1-11, wherein a least one output of the analog crosspoint switch includes a high current driver circuit
13. The method of clauses 1-11, wherein at least one input of the analog crosspoint switch includes a voltage limiting circuit
14. The method of clauses 1-11, wherein at least one input of the analog crosspoint switch includes a current limiting circuit
15. The method of clauses 1-11, wherein the analog crosspoint switch comprises a plurality of interconnected relays that facilitate connecting any of a plurality of input to any of a plurality of outputs
16. The method of clauses 1-11, wherein the analog crosspoint switch further comprises an analog to digital converter that converts a portion of analog signals input to the crosspoint switch into a representative digital signal
17. The method of clauses 1-11, the analog crosspoint switch further comprising signal processing functionality to detect one or more analog input signal conditions and in response thereto perform an action [set an alarm, change switch configuration, disable one or more outputs, power off a portion of the switch, change a state of a general purpose (digital/analog) output, etc]
18. The method of clauses 1-11, wherein a portion of the outputs are analog outputs and a portion of the outputs are digital outputs
19. The method of clauses 1-11, wherein the analog crosspoint switch is adapted to detect one or more analog input signal conditions.
20. The method of clauses 1-19, wherein the analog crosspoint switch is adapted to take one or more actions in response to detecting the one or more analog input signal conditions, the one more actions selected from a list consisting of setting an alarm, sending an alarm signal, changing a configuration of the analog crosspoint switch, disabling an output, powering off a portion of the analog crosspoint switch, powering on a portion of the analog crosspoint switch, and control a general purpose output of the analog crosspoint switch.
21. A system for monitoring a power roller of a conveyor in an industrial environment comprising;
a plurality of sensors disposed to sense conditions of the power roller, wherein the sensors produce analog signals representative of the sensed conditions; and
an analog crosspoint switch comprising a plurality of inputs and a plurality of outputs, wherein the sensor produced analog signals connect to a portion of the plurality of inputs;
wherein the analog crosspoint switch is configurable to switch a portion of the input analog signals representing sensed conditions of the power roller to a plurality of the outputs.
22. The system of clauses 1-21, wherein the conditions of the power roller that are sensed by the plurality of sensors comprise at least one of rate of rotation of the power roller, a load being transported by the roller, power consumed by the power roller, and a rate of acceleration of the power roller.
23. A system for monitoring a fan in a factory setting, comprising:
a plurality of sensors disposed to sense conditions of the fan in the factory setting, wherein the sensors produce analog signals representative of the sensed conditions; and
an analog crosspoint switch comprising a plurality of inputs and a plurality of outputs, wherein the sensor produced analog signals connect to a portion of the plurality of inputs;
wherein the analog crosspoint switch is configurable to switch a portion of the input analog signals representing sensed conditions of the fan to a plurality of the outputs.
24. The system of clauses 1-23, wherein the conditions of the fan in a factory setting that are sensed by the plurality of sensors comprise at least one of fan blade tip speed, torque, back pressure, revolutions per minute and volume of air per unit time produced by the fan.
25. A system for monitoring a turbine in a power generation environment, comprising:
a plurality of sensors disposed to sense conditions of the turbine, wherein the sensors produce analog signals representative of the sensed conditions; and
an analog crosspoint switch comprising a plurality of inputs and a plurality of outputs, wherein the sensor produced analog signals connect to a portion of the plurality of inputs;
wherein the analog crosspoint switch is configurable to switch a portion of the input analog signals representing sensed conditions of the turbine to a plurality of the outputs.
26. The system for monitoring a turbine in a power generation environment of clause 25, wherein the sensed conditions are selected from the list consisting of: a relative shaft vibration, an absolute vibration of bearings, a turbine cover vibration, a thrust bearing axial vibration, a stator core vibration, a stator bar vibration, and a stator end winding vibrations.
In embodiments, methods and systems of data collection in an industrial environment may include a plurality of industrial condition sensing and acquisition modules that may include at least one programmable logic component per module that may control a portion of the sensing and acquisition functionality of its module. The programmable logic components on each of the modules may be disposed on a condition sensing module. The programmable logic components on each of the modules may be interconnected by a communication bus, such as a dedicated logic bus, that may include data and control channels. The dedicated logic bus may extend logically and/or physically to other programmable logic components on other sensing and acquisition modules. In embodiments, the programmable logic components may be programmed via the communication bus or dedicated interconnection bus, via a dedicated programming portion of the dedicated communication bus or interconnection bus, via a program that is passed between programmable logic components, sensing and acquisition modules, or whole systems. A programmable logic component for use in an industrial environment data sensing and acquisition system may be a Complex Programmable Logic Device, an Application-Specific Integrated Circuit, microcontrollers, field programmable arrays (FPGAs), and combinations thereof.
A programmable logic component in an industrial data collection environment may perform control functions associated with data collection. Control examples include power control of analog channels, sensors, analog receivers, analog switches, sensors, multiplexors, portions of logic modules (e.g., a logic board, system and the like) on which the programmable logic component is disposed, a sleep mode of the programmable logic component, a self-power-up/down, self-sleep/wake up, and other functions of the programmable logic component, the like. Control functions, such as these and others, may be performed in coordination with control and operational functions of other programmable logic components, such as other components on a single data collection module and components on other such modules. Other functions that a programmable logic component may provide may include generation of a voltage reference, such as a precise voltage reference for input signal condition detection, a sensor, an analog to digital convertor disposed on the module, and the like. A programmable logic component may generate, set, reset, adjust, calibrate, or otherwise determine the voltage of the reference, its tolerance, and the like. Other functions of a programmable logic component may include enabling a digital phase lock loop to facilitate tracking slowly transitioning input signals, and further to facilitate detecting the phase of such signals. Relative phase detection may also be implemented, including phase relative to trigger signals, other analog inputs, such as from a corresponding sensor on the module, on-board references (e.g., on-board timers), and the like. A programmable logic component may be programmed to perform input signal peak voltage detection and control input signal circuitry, such as to implement auto-scaling of the input to an operating voltage range of the input. Other functions that may be programmed into a programmable logic component may include determining an appropriate sampling frequency for sampling inputs independently of their operating frequencies. A programmable logic component may be programmed to detect a maximum frequency among a plurality of input signals and set a sampling frequency for each of the input signals that is greater than the detected maximum frequency. A programmable logic component may be programmed to control a sampling of a sensor on the module.
A programmable logic component may be programmed to configure a multiplexer by specifying to the multiplexer a mapping of input to output. A programmable logic component may be programmed to configure and control data routing components, such as multiplexers, crosspoint switches, analog to digital converters, and the like, to implement a data collection template for the industrial environment. A smart band data collection template may be included in a program for a programmable logic component. Alternatively, an algorithm that interprets a data collection template to configure and control data routing resources in the industrial environment may be include in the program.
In embodiments, one or more programmable logic components in an industrial environment may be programmed to perform smart-band signal analysis and testing. Results of such analysis and testing may include triggering smart band data collection actions, that may include reconfiguring one or more data routing resources in the industrial environment. A programmable logic component may be configured to perform a portion of smart band analysis, such as collection and validation of signal activity from one or more sensors that may be local to the programmable logic component. Smart band signal analysis results from a plurality of programmable logic components may be further processed by other programmable logic components, servers, machine learning systems, and the like to determine compliance with a smart band.
In embodiments, one or more programmable logic components in an industrial environment may be programmed to control data routing resources and sensors for outcomes, such as reducing power consumption (e.g., powering on/off resources as needed), implement security in the industrial environment by managing user authentication, and the like. In embodiments, certain data routing resources, such as multiplexers and the like, may be configured to support certain input signal types. A programmable logic component may configure the resources based on the type of signals to be routed to the resources. In embodiments, the programmable logic component may facilitate coordination of sensor and data routing resource signal type matching by indicating to a configurable sensor a protocol or signal type to present to the routing resource. A programmable logic component may facilitate detecting a protocol of a signal being input to a data routing resource, such as an analog crosspoint switch and the like. Based on the detected protocol, the programmable logic component may configure routing resources to facilitate support and efficient processing of the protocol. In an example, a programmable logic component configured as a data collection module in an industrial environment may include an algorithm for implementing an intelligent sensor interface specification, such as IEEE1451.2 intelligent sensor interface specification.
In embodiments, distributing programmable logic components across a plurality of data sensing, collection, and/or routing modules in an industrial environment may facilitate greater functionality and local inter-operational control. In an example, modules may perform operational functions independently based on a program installed in one or more programmable logic components associated with each module. Two modules may be constructed with substantially identical physical components, but may perform different functions in the industrial environment based on the program(s) loaded into programmable logic component(s) on the modules. In this way, even if one module were to experience a fault, or be powered down, other modules may continue to perform their functions due at least in part to each having its own programmable logic component(s). In embodiments, configuring a plurality of programmable logic components distributed across a plurality of data collection modules in an industrial environment may facilitate scalability in terms of conditions in the environment that may be sensed, number of data routing options for routing sensed data throughout the industrial environment, types of conditions that may be sensed, computing capability in the environment, and the like.
In embodiments, a programmable logic controller-configured data collection and routing system may facilitate validation of external systems for use as storage nodes, such as for a distributed ledger, and the like. A programmable logic component may be programmed to perform validation of a protocol for communicating with such an external system, such as an external storage node.
In embodiments, programming of programmable logic components, such as CPLDs and the like may be performed to accommodate a range of data sensing, collection and configuration differences. In embodiments, reprogramming may be performed on one or more components when adding and/or when removing sensors, when changing sensor types, when changing sensor configurations or settings, when changing data storage configurations, when embedding smart band data collection template(s) into device programs, when adding and/or removing data collection modules (e.g., scaling a system), when a lower cost device is used that may limit functionality or resources over a higher costs device, and the like. A programmable logic component may be programmed to propagate programs for other programmable components via a dedicated programmable logic device programming channel, via a daisy chain programming architecture, via a mesh of programmable logic components, via a hub-and-spoke architecture of interconnected components, via a ring configuration (e.g., using a communication token, and the like).
In embodiments, a system for data collection in an industrial environment comprising distributed programmable logic devices connected by a dedicated control bus may be deployed with drilling machines in an oil and gas harvesting environment, such as an oil and/or gas field. A drilling machine has many active portions that may be operated, monitored, and adjusted during a drilling operation. Sensors to monitor a crown block may be physically isolated from sensors for monitoring a blowout preventer and the like. To effectively maintain control of this wide range and diverse disposition of sensors, programmable logic components, such as Complex Programmable Logic Devices (CPLDs) may be distributed throughout the drilling machine. While each CPLD may be configured with a program to facilitate operation of a limited set of sensors, at least portions of the CPLDs may be connected by a dedicated bus for facilitating coordination of sensor control, operation and use. In an example, a set of sensors may be disposed proximal to a mud pump or the like to monitor flow, density, mud tank levels, and the like. One or more CPLDs may be deployed with each sensor (or a group of sensors) to operate the sensors and sensor signal routing and collection resources. The CPLDs in this mud pump group may be interconnected by a dedicated control bus to facilitate coordination of sensor and data collection resource control and the like. This dedicated bus may extend physically and/or logically beyond the mud pump control portion of the drill machine so that CPLDs of other portions (e.g., the crown block and the like) may coordinate data collection and related activity through portions of the drilling machine.
In embodiments, a system for data collection in an industrial environment comprising distributed programmable logic devices connected by a dedicated control bus may be deployed with compressors in an oil and gas harvesting environment, such as an oil and/or gas field. Compressors are used in the oil and gas industry for compressing a variety of gases and purposes include flash gas, gas lift, reinjection, boosting, vapor-recovery, casing head and the like. Collecting data from sensors for these different compressor functions may require substantively different control regimes. Distributing CPLDs programmed with different control regimes is an approach that may accommodate these diverse data collection requirements. One or more CPLDs may be disposed with sets of sensors for the different compressor functions. A dedicated control bus may be used to facilitate coordination of control and/or programming of CPLDs in and across compressor instances. In an example, a CPLD may be configured to manage a data collection infrastructure for sensors disposed to collect compressor-related conditions for flash gas compression; a second CPLD or group of CPLDs may be configured to manage a data collection infrastructure for sensors disposed to collect compressor related conditions for vapor-recovery gas compression. These groups of CPLDs may operate control programs
In embodiments, a system for data collection in an industrial environment comprising distributed programmable logic devices connected by a dedicated control bus may be deployed in a refinery with turbines for oil and gas production, such as with modular impulse steam turbines. A system for collection of data from impulse steam turbines may be configured with a plurality of condition sensing and collection modules adapted for specific functions of an impulse steam turbine. Distributing CPLDs along with these modules can facilitate adaptable data collection to suit individual installations. As an example, blade conditions, such as tip rotational rate, temperature rise of the blades, impulse pressure, blade acceleration rate, and the like may be captured in data collection modules configured with sensors for sensing these conditions. Other modules may be configured to collect data associated with valves (e.g., in a multi-valve configuration, one or more modules may be configured for each valve or for a set of valves), turbine exhaust (e.g., radial exhaust data collection may be configured differently than axial exhaust data collection), turbine speed sensing may be configured differently for fixed versus variable speed implementations, and the like. Additionally, impulse gas turbine systems may be installed with other systems, such as combined cycle systems, cogeneration systems, solar power generation systems, wind power generation systems, hydropower generation systems, and the like. Data collection requirements for these installations may also vary. Using a CPLD-based, modular data collection system that uses a dedicated interconnection bus for the CPLDs may facilitate programming and/or reprogramming of each module directly in-place without having to shut down or physically access each module.
Referring to
1. A system for data collection in an industrial environment comprising:
a plurality of industrial condition sensing and acquisition modules;
at least one programmable logic component disposed on each of the plurality of modules, the at least one programmable logic component controlling a portion of the sensing and acquisition functionality of a module on which it is disposed; and
a communication bus that is dedicated to interconnecting the at least one programmable logic component disposed on at least one of the plurality of modules, wherein the communication bus extends to other programmable logic components on other sensing and acquisition modules.
2. The system of clause 1, wherein a programmable logic component is programmed via the communication bus.
3. The system of clause 1, wherein the communication bus includes a portion that is dedicated to programming the programmable logic components.
4. The system of clause 1, wherein controlling a portion of the sensing and acquisition functionality of a module comprises at least on power control function selected from a list consisting of controlling power of a sensor, a multiplexer, a portion of the module, and controlling sleep mode of the programmable logic component.
5. The system of clause 1, wherein controlling a portion of the sensing and acquisition functionality of a module comprises providing a voltage reference to at least one of a sensor and an analog to digital converter disposed on the module.
6. The system of clause 1, wherein controlling a portion of the sensing and acquisition functionality of a module comprises detecting relative phase of at least two analog signals derived from at least two sensors disposed on the module.
7. The system of clause 1, wherein controlling a portion of the sensing and acquisition functionality of a module comprises controlling sampling of data provided by at least one sensor disposed on the module.
8. The system of clause 1, wherein controlling a portion of the sensing and acquisition functionality of a module comprises detecting a peak voltage of a signal provided by a sensor disposed on the module.
9. The system of clause 1, wherein controlling a portion of the sensing and acquisition functionality of a module comprises configuring at least one multiplexer disposed on the module by specifying to the multiplexer a mapping of at least one input and one output.
10. A system for data collection in an industrial environment comprising:
at least one programmable logic component disposed on a condition sensing module, the at least one programmable logic component controlling a portion of the condition sensing module on which it is disposed; and a communication bus through which a plurality of programmable logic components facilitate control of the system, wherein the communication bus extends to other programmable logic components on other condition sensing modules.
11. The system of clause 10, wherein the communication bus includes a portion that is dedicated to programming the programmable logic components.
12. The system of clause 10, wherein controlling a portion of the sensing and acquisition functionality of a module comprises at least on power control function selected from a list consisting of controlling power of a sensor, a multiplexer, a portion of the module, and controlling sleep mode of the programmable logic component.
13. The system of clause 10, wherein controlling a portion of the sensing and acquisition functionality of a module comprises providing a voltage reference to at least one of a sensor and an analog to digital converter disposed on the module.
14. The system of clause 10, wherein controlling a portion of the sensing and acquisition functionality of a module comprises detecting relative phase of at least two analog signals derived from at least two sensors disposed on the module.
15. The system of clause 10, wherein controlling a portion of the sensing and acquisition functionality of a module comprises controlling sampling of data provided by at least one sensor disposed on the module.
16. A method of data collection in an industrial environment comprising:
disposing at least one programmable logic component on each of a plurality of industrial environment condition sensing modules;
programming the at least one programmable logic component disposed on each of the plurality of modules with a module control program; and
communicating among programmable logic components on the plurality of sensing modules via a communication bus that is dedicated to interconnecting a plurality of programmable logic components, wherein the communication bus extends to other programmable logic components on other modules of the plurality of industrial environment condition sensing modules.
17. The method of clause 16, wherein the module control program comprises an algorithm for implementing an intelligent sensor interface communication protocol.
18. The method of clause 17, wherein the intelligent sensor interface communication protocol is compatible with IEEE1451.2 intelligent sensor interface communication protocol.
19. The method of clause 17, wherein programming the at least one programmable logic component comprises configuring the programmable logic component to implement a smart band data collection template.
20. The method of clause 17, wherein the programmable logic component type is selected from the list consisting of field programmable gate arrays, complex programmable logic devices, and microcontrollers.
21. A system for monitoring a drilling machine for oil and gas field use comprising:
a plurality of industrial condition sensing and acquisition modules disposed to monitor portions of the drilling machine;
at least one programmable logic component disposed on each of the plurality of modules, the at least one programmable logic component controlling a portion of the sensing and acquisition functionality of a module on which it is disposed; and
a communication bus that is dedicated to interconnecting the at least one programmable logic component disposed on at least one of the plurality of modules, wherein the communication bus extends to other programmable logic components on other sensing and acquisition modules.
22. A system for monitoring a compressor for oil and gas field use comprising:
a plurality of industrial condition sensing and acquisition modules disposed to monitor portions of the compressor;
at least one programmable logic component disposed on each of the plurality of modules, the at least one programmable logic component controlling a portion of the sensing and acquisition functionality of a module on which it is disposed; and
a communication bus that is dedicated to interconnecting the at least one programmable logic component disposed on at least one of the plurality of modules, wherein the communication bus extends to other programmable logic components on other sensing and acquisition modules.
23. A system for monitoring an impulse steam turbine comprising:
a plurality of industrial condition sensing and acquisition modules disposed to monitor portions of the impulse steam engine;
at least one programmable logic component disposed on each of the plurality of modules, the at least one programmable logic component controlling a portion of the sensing and acquisition functionality of a module on which it is disposed; and
a communication bus that is dedicated to interconnecting the at least one programmable logic component disposed on at least one of the plurality of modules, wherein the communication bus extends to other programmable logic components on other sensing and acquisition modules.
In embodiments, a system for data collection in an industrial environment may include a trigger signal and at least one data signal that share a common output of a signal multiplexer and upon detection of a condition in the industrial environment, such as a state of the trigger signal, the common output is switched to propagate either the data signal or the trigger signal. Sharing an output between a data signal and a trigger signal may also facilitate reducing a number of individually routed signals in an industrial environment. Benefits of reducing individually routed signals may include reducing the number of interconnections between data collection module, thereby reducing the complexity of the industrial environment. Trade-offs for reducing individually routed signals may include increasing sophistication of logic at signal switching modules to implement the detection and conditional switching of signals. A net benefit of this added localized logic complexity maybe an overall reduction in the implementation complexity of such a data collection system in an industrial environment.
Exemplary deployment environments may include environments with trigger signal channel limitations, such as existing data collection systems that do not have separate trigger support for transporting an additional trigger signal to a module with sufficient computing sophistication to perform trigger detection. Another exemplary deployment may include systems that require at least some autonomous control for performing data collection.
In embodiments, a system for data collection in an industrial environment may include an analog switch that switches between a first input, such as a trigger input and a second input, such as a data input based on a condition of the first input. A trigger input may be monitored by a portion of the analog switch to detect a change in the signal, such as from a lower voltage to a higher voltage relative to a reference or trigger threshold voltage. In embodiments, a device that may receive the switched signal from the analog switch may monitor the trigger signal for a condition that indicates a condition for switching the output from the trigger input to the data input. When a condition of the trigger input is detected, the analog switch maybe reconfigured, to direct the data input to the same output that was propagating the trigger output.
In embodiment, a system for data collection in an industrial environment may include an analog switch that directs a first input to an output of the analog switch until such time as the output of the analog switch indicates that a second input should be directed to the output of the analog switch. The output of the analog switch may cause an alarm to be generated. The output of the analog switch may propagate a trigger signal to the output. In response to the trigger signal propagating through the switch transitioning from a first condition (e.g., a first voltage below a trigger threshold voltage value) to a second condition (e.g., a second voltage above the trigger threshold voltage value), the switch may stop propagating the trigger signal and instead propagate another input signal to the output. In embodiments, the trigger signal and the other data signal may be related, such as the trigger signal may indicate a presence of an object being placed on a conveyer and the data signal represents a strain placed on the conveyer.
In embodiments, to facilitate timely detection of the trigger condition, a rate of sampling of the output of the analog switch may be adjustable, so that for example, the rate of sampling is higher while the trigger signal is propagated and lower when data signal is propagated. Alternatively, a rate of sampling may be fixed for either the trigger or the data signal. In embodiments, the rate of sampling may be based on a predefined time from trigger occurrence to trigger detection and may be faster than a minimum sample rate to capture the data signal. Alternatively, a rate of sampling may exceed a rate of transition for a plurality of the input signals.
In embodiments, routing a plurality of hierarchically organized triggers onto another analog channel may facilitate implementing a hierarchical data collection triggering structure in an industrial environment. A data collection template to implement a hierarchical trigger signal architecture may include signal switch configuration and function data that may facilitate a signal switch facility, such as an analog crosspoint switch or multiplexer to output a first input trigger in a hierarchy and based on the first trigger condition being detected output a second input trigger in the hierarchy on the same output as the first input trigger by changing an internal mapping if inputs to outputs. Upon detection of the second input trigger condition, the output may be switched to a data signal, such as data from a sensor in an industrial environment.
In embodiments, upon detection of a trigger condition, in addition to switching from the trigger signal to a data signal, an alarm may be generated and optionally propagated to a higher functioning device/module. In addition to switching to a data signal, upon detection of a state of the trigger, sensors that otherwise may be disabled or powered down may be energized/activated to begin to produce data for the newly selected data signal. Activating might alternatively include sending a reset or refresh signal to sensor(s).
In embodiments, a system for data collection in an industrial environment may include a system for routing a trigger signal onto a data signal path in association with a gearbox of an industrial vehicle. Combining a trigger signal onto a signal path that is also used for a data signal may be useful in gearbox applications by reducing the number of signal lines that need to be routed, while enabling advanced functions, such as data collection based on pressure changes in the hydraulic fluid and the like. As an example, a sensor may be configured to detect a pressure difference in the hydraulic fluid that exceeds a certain threshold as may occur when the hydraulic fluid flow is directed back into the impeller to give higher torque at low speeds. The output of such a sensor may be configured as a trigger for collecting data about the gearbox when operating at low speeds. In an example, a data collection system for an industrial environment may have a multiplexer or switch that facilitates routing either a trigger or a data channel over a single signal path. Detecting the trigger signal from the pressure sensor may result in a different signal being routed through the same line that the trigger signal was routed, by switching, for example a set of controls a multiplexer that outputs the trigger signal until the trigger signal is detected as indicating that the output should be changed to the data signal. As a result of detecting the high-pressure condition, a data collection activity may be activated so that data can be collected using the same line as was recently used by the trigger signal.
In embodiments, a system for data collection in an industrial environment may include a system for routing a trigger signal onto a data signal path in association with a vehicle suspension for truck and car operation. Vehicle suspension, particularly active suspension may include sensors for detecting road events, suspension conditions, vehicle data, such as speed, steering, and the like. These conditions may not always need to be detected, except, for example, upon detection of a trigger condition. Therefore, combining the trigger condition signal and at least one data signal on a single physical signal routing path could be implemented. Doing so may reduce costs due to fewer physical connections required in such a data collection system. In an example, a sensor may be configured to detect a condition, such as a pot hole, that the suspension must react to. Data from the suspension may be routed along the same signal routing path as this road condition trigger signal so that upon detection of the pot hole, data may be collected that may facilitate determining aspects of the suspension's reaction to the pot hole.
In embodiments, a system for data collection in an industrial environment may include a system for routing a trigger signal onto a data signal path in association with a turbine for power generation in a power station. A turbine used for power generation may be retrofitted with a data collection system that optimizes existing data signal lines to implement greater data collection functions. One such approach involves routing new sources of data over existing lines. While multiplexing signals generally satisfies this need, combining a trigger signal with a data signal via a multiplexer or the like can further improve data collection. In an example, a first sensor may include a thermal threshold sensor that may measure the temperature of an aspect of a power generation turbine. Upon detection of that trigger (e.g., by the temperature rising above the thermal threshold), a data collection system controller may send a different data collection signal over the same line that was used to detect the trigger condition. This may be accomplished by a controller or the like sensing the trigger signal change condition and then signaling to the multiplexer to switch from the trigger signal to a data signal to be output on the same line as the trigger signal for data collection. In the example, when a turbine is detected as having a portion that exceeds its safe thermal threshold, a secondary safety signal may be routed over the trigger signal path and monitored for additional safety conditions, such as overheating and the like.
Referring to
1. A system for data collection in an industrial environment comprising an analog switch that directs a first input to an output of the analog switch until such time as the output of the analog switch indicates that a second input should be directed to the output of the analog switch.
2. The system of clause 1, wherein the output of the analog switch indicated that the second input should be directed to the output based on the output transitioning from a pending condition to a triggered condition.
3. The system of clause 2, wherein the triggered condition comprises detecting the output presenting a voltage above a trigger voltage value.
4. The system of clause 1, further comprising routing a plurality of signals with the analog switch from inputs on the analog switch to outputs on the analog switch in response to the output of the analog switch indicating that the second input should be directed to the output.
5. The system of clause 1, further comprising sampling the output of the analog switch at a rate that exceeds a rate of transition for a plurality of signals input to the analog switch.
6. The system of clause 1, further comprising generating an alarm signal when the output of the analog switch indicates that a second input should be directed to the output of the analog switch.
7. A system for data collection in an industrial environment comprising an analog switch that switches between a first input and a second input based on a condition of the first input.
8. The system of clause 7, wherein the condition of the first input comprises the first input presenting a triggered condition.
9. The system of clause 8, wherein the triggered condition comprises detecting the first input presenting a voltage above a trigger voltage value.
10. The system of clause 7, further comprising routing a plurality of signals with the analog from inputs on the analog switch to outputs on the analog switch based on the condition of the first input.
11. The system of clause 7, further comprising sampling an input of the analog switch at a rate that exceeds a rate of transition for a plurality of signals input to the analog switch.
12. The system of clause 7, further comprising generating an alarm signal based on the condition of the first input.
13. A system for data collection in an industrial environment comprising a trigger signal and at least one data signal that share a common output of a signal multiplexer and upon detection of a predefined state of the trigger signal, the common output is configured to propagate the at least one data signal through the signal multiplexer.
14. The system of clause 13, wherein the signal multiplexer is an analog multiplexer.
15. The system of clause 13, wherein the predefined state of the trigger signal is detected on the common output.
16. The system of clause 13, wherein detection of the predefined state of the trigger signal comprises detecting the common output presenting a voltage above a trigger voltage value.
17. The system of clause 13, further comprising routing a plurality of signals with the multiplexer from inputs on the multiplexer to outputs on the multiplexer in response to detection of the predefined state of the trigger signal.
18. The system of clause 13, further comprising sampling the output of the multiplexer at a rate that exceeds a rate of transition for a plurality of signals input to the multiplexer.
19. The system of clause 13, further comprising generating an alarm in response to detection of the predefined state of the trigger signal.
20. The system of clause 13, further comprising activating at least one sensor to produce the at least one data signal.
21. A system for monitoring a gearbox of an industrial vehicle comprising an analog switch that directs a trigger signal representing a condition of the gearbox to an output of the analog switch until such time as the output of the analog switch indicates that a second input representing a condition of the gearbox related to the trigger signal should be directed to the output of the analog switch.
22. A system for monitoring a suspension of an industrial vehicle comprising an analog switch that directs a trigger signal representing a condition of the suspension to an output of the analog switch until such time as the output of the analog switch indicates that a second input representing a condition of the suspension related to the trigger signal should be directed to the output of the analog switch.
23. A system for monitoring a power generation turbine comprising an analog switch that directs a trigger signal representing a condition of the power generation turbine to an output of the analog switch until such time as the output of the analog switch indicates that a second input representing a condition of the power generation turbine related to the trigger signal should be directed to the output of the analog switch.
In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors at least one signal for a set of collection band parameters and upon detection of a parameter from the set of collection band parameters in the signal, configures collection of data from a set of sensors based on the detected parameter. The set of selected sensors, the signal and the set of collection band parameters may be part of a smart-bands data collection template that may be used by the system when collecting data in an industrial environment. A motivation for preparing a smart-bands data collection template may include monitoring a set of conditions of an industrial machine to facilitate improved operation, reduced down time, preventive maintenance, failure prevention, and the like. Based on analysis of data about the industrial machine, such as those conditions that may be detected by the set of sensors, an action may be taken, such as notifying a user of a change in the condition, adjusting operating parameters, scheduling preventive maintenance, triggering data collection from additional sets of sensors and the like. An example of data that may indicate a need for some action may include changes that may be detectable through trends present in the data from the set of sensors. Another example is trends of analysis values derived from the set of sensors.
In embodiments, the set of collection band parameters may include values received from a sensor that is configured to sense a condition of the industrial machine (e.g., bearing vibration). However, a set of collection band parameters may instead be a trend of data received from the sensor (e.g., a trend of bearing vibration across a plurality of vibration measurements by a bearing vibration sensor). In embodiments, a set of collection band parameters may be a composite of data and/or trends of data from a plurality of sensors (e.g., a trend of data from on-axis and off-axis vibration sensors). In embodiments, when a data value derived from one or more sensors as described herein is sufficiently close to a value of data in the set of collection band parameters, the data collection activity from the set of sensors may be triggered. Alternatively, a data collection activity from the set of sensors may be triggered when a data value derived from the one or more sensors (e.g., trends and the like) falls outside of a set of collection band parameters. In an example, a set of data collection band parameters for a motor may be a range of rotational speeds from 95% to 105% of a select operational rotational speed. So long as a trend of rotational speed of the motor stays within this range, a data collection activity may be deferred. However, when the trend reaches or exceeds this range, then a data collection activity, such as one defined by a smart bands data collection template may be triggered.
In embodiments, triggering a data collection activity, such as one defined by a smart bands data collection template, may result in a change to a data collection system for an industrial environment that may impact aspects of the system such as data sensing, switching, routing, storage allocation, storage configuration, and the like. This change to the data collection system may occur in near real time to the detection of the condition; however, it may be scheduled to occur in the future. It may also be coordinated with other data collection activities so that active data collection activities, such as a data collection activity for a different smart band data collection template, can complete prior to the system being reconfigured to meet the smart bands data collection template that is triggered by the sensed condition meeting the smart bands data collection trigger.
In embodiments, processing of data from sensors may be cumulative over time, over a set of sensors, across machines in an industrial environment, and the like. While a sensed value of a condition may be sufficient to trigger a smart bands data collection template activity, data may need to be collected and processed over time from a plurality of sensors to generate a data value that may be compared to a set of data collection band parameters for conditionally triggering the data collection activity. Using data from multiple sensors and/or processing data, such as to generate a trend of data values and the like may facilitate preventing inconsequential instances of a sensed data value being outside of an acceptable range from causing unwarranted smart bands data collection activity. In an example, if a vibration from a bearing is detected outside of an acceptable range infrequently, then trending for this value over time may be useful to detect if the frequency is increasing, decreasing or staying substantially constant or within a range of values. If the frequency of such a value is found to be increasing, then such a trend is indicative of changes occurring in operation of the industrial machine as experienced by the bearing. An acceptable range of values of this trended vibration value may be established as a set of data collection band parameters against which vibration data for the bearing will be monitored. When the trended vibration value is outside of this range of acceptable values, a smart bands data collection activity may be activated.
In embodiments a system for data collection in an industrial environment that supports smart band data collection templates may be configured with data processing capability at a point of sensing of one or more conditions that may trigger a smart bands data collection template data collection activity, such as by use of an intelligent sensor that may include data processing capabilities, by use of a programmable logic components that interfaces with a sensor and processes data from the sensor, by a computer processor, such as a microprocessor and the like disposed proximal to the sensor, and the like. In embodiments, processing of data collected from one or more sensors for detecting a smart bands template data collection activity may be performed by remote processors, servers, and the like that may have access to data from a plurality of sensors, sensor modules, industrial machines, industrial environments, and the like.
In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors an industrial environment for a set of parameters, and upon detection of at least one parameter configures collection of data from a set of sensors and causes a data storage controller to adapt a configuration of data storage facilities to support collection of data from the set of sensors based on the detected parameter. The methods and systems described herein for conditionally changing a configuration of a data collection system in an industrial environment to implement a smart bands data collection template may further include changes to data storage architectures. As an example, a data storage facility may be disposed on a data collection module that may include one or more sensors for monitoring conditions in an industrial environment. This local data storage facility may typically be configured for rapid movement of sensed data from the module to a next level sensing or processing module or server. When a smart bands data collection conditions is detected, sensor data from a plurality of sensors may need to be captured concurrently. To accommodate this concurrent collection, the local memory may be reconfigured to capture data from each of the plurality of sensors in a coordinated manner, such as sampling each of the sensors synchronously, or with a known offset, and the like repeatedly to build up a set of sensed data that may be much larger than would typically be captured and moved through the local memory. A storage control facility for controlling the local storage may monitor the movement of sensor data into and out of the local data storage, thereby ensuring safe movement of data from the plurality of sensors to the local data storage and on to a destination, such as a server, networked storage facility and the like. The local data storage facility may be configured so that data from the set of sensors associated with a smart bands data collection template are securely storage and readily accessible as a set of smart band data to facilitate processing the smart band-specific data. As an example, local storage may comprise non-volatile memory (NVM). To prepare for data collection in response to a smart band data collection template being triggered, portions of the NVM may be erased to prepare the NVM to receive data as indicated in the template.
In embodiments, sensors may be arranged into a set of sensors for condition-specific monitoring. Each set, which may be a logical set of sensors, may be selected to provide information about elements in an industrial environment that may provide insight into potential problems, root causes of problems and the like. Each set may be associated with a condition that may be monitored for compliance with an acceptable range of values. The set of sensors may be based on a machine architecture, hierarchy of components, hierarchy of data that contributes to a finding about a machine that may usefully be applied to maintaining or improving performance in the industrial environment. Smart band sensor sets may be configured based on expert system analysis of complex conditions, such as machine failures and the like. Smart band sensor sets may be arranged to facilitate knowledge gathering independent of a particular failure mode or history. Smart band sensor sets may be arranged to test a suggested smart band data collection template prior to implementing it as part of an industrial machine operations program. Gathering and processing data from sets of sensors may facilitate determining which sensors contribute meaningful data to the set and those sensors that do not contribute can be removed from the set. Smart band sensor sets may be adjusted based on external data, such as industry studies that indicate the types of sensor data that is most to help reduce failures in an industrial environment.
In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors an industrial environment for a set of parameters and upon detection of at least one parameter configures collection of data from a set of sensors based on the detected parameter.
In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors at least one information technology element for a capacity parameter and upon detection of the parameter configures collection of data from a set of sensors based on the detected parameter. In embodiments, the capacity parameter may be a bandwidth parameter and/or a storage parameter.
In embodiments, a system for data collection in an industrial environment may include a data collection system that monitors at least one signal for compliance to a set of collection band conditions and upon detection of a lack of compliance sets about collecting data from a predetermined set of sensors associated with the monitored signal. Upon detection of a lack of compliance, a collection band template associated with the monitored signal may be accessed and resources identified in the template may be configured to perform the data collection. In embodiments, the template may identify sensors to activate, data from the sensors to collect, duration of collection or quantity of data to be collected, destination (e.g., memory structure) to store the collected data, and the like. In embodiments, a smart-band method for data collection in an industrial environment may include periodic collection of data from one or more sensors configured to sense a condition of an industrial machine in the environment. The collected data may be checked against a set of criteria that define an acceptable range of the condition. Upon validation that the collected data is either approaching one end of the acceptable at a rate beyond an acceptable limit or is beyond the acceptable range of the condition, collecting data may commence from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a data collection template. In embodiments, an acceptable range of the condition is based on a history of applied analytics of the condition. In embodiments, upon validation of the acceptable range being exceeded, data storage resources of a module in which the sensed condition is detected may be configured to facilitate capturing data from the smart-band group of sensors.
In embodiments, a system for data collection in an industrial environment may include a data collection system configured with a machine learning capability that monitors an industrial environment for a set of parameters, learns a range of acceptable values for the set of parameters, and upon detection of at least one instance of a parameter that is outside of the acceptable range of values, configures collection of data from a set of sensors based on the detected parameter. In embodiments, the machine learning capability may be a neural net expert system, a fuzzy logic expert system, and the like.
In embodiments, monitoring a condition to trigger a smart band data collection template data collection action may be: in response to a regulation, such as a safety regulation; in response to an upcoming activity, such as a portion of the industrial environment being shut down for preventive maintenance; in response to sensor data missing from routine data collection activities; and the like. In embodiments, in response to a faulty sensor or sensor data missing from a smart band template data collection activity, one or more alternate sensors may be temporarily included in the set of sensors so as to provide data that may effectively substitute for the missing data in data processing algorithms.
In embodiments, smart band data collection templates may be configured for detecting and gathering data for smart band analysis covering vibration spectra, such as vibration envelope and current signature for spectral regions or peaks that may be combinations of absolute frequency or factors of machine related parameters, vibration time waveforms for time-domain derived calculations including, without limitation RMS overall, peak overall, true peak, crest factor, and the like, vibration vectors, spectral energy humps in various regions (e.g., low-frequency region, high frequency region, low orders, and the like), pressure-volume analysis and the like.
In embodiments, a system for data collection that applies smart band data collection templates may be applied to an industrial environment, such as ball screw actuators in an automated production environment. Smart band analysis may be applied to ball screw actuators in industrial environments such as precision manufacturing or positioning applications (e.g., semiconductor photolithography machines, and the like). As a typical primary objective of using a balls screw is for precise positioning, detection of variation in the positioning mechanism can help avoid costly defective production runs. Smart bands triggering and data collection may help in such applications by detecting, through smart band analysis potential variations in the positioning mechanism, such as the ball, screw, and the like. In an example, data related to a ball screw positioning system may be collected with a system for data collection in an industrial environment as described herein. A plurality of sensors may be configured to collect data such as screw torque, screw direction screw speed, screw step, home detection, and the like. Some portion of this data may be processed by a smart bands data analysis facility to determine if variances, such as trends in screw speed as a function of torque, approach or exceed an acceptable threshold. Upon such a determination, a data collection template for the ball screw production system may be activated to configure the data sensing, routing and collection resources of the data collection system to perform data collection to facilitate further analysis. The smart band data collection template facilitates rapid collection of data from other sensors than screw speed and torque, such as position, direction, acceleration, and the like by routing data from corresponding sensors over one or more signal paths to a data collector. The duration and order of collection of the data from these sources may be specified in the smart bands data collection template so that data required for further analysis is effectively captured.
In embodiments, a system for data collection that applies smart band data collection templates to configure and utilize data collection and routing infrastructure may be applied to ventilation systems in mining environments. Ventilation provides a crucial role in mining safety. Early detection of potential problems with ventilation equipment can be aided by applying a smart bands approach to data collection in such an environment. Sensors may be disposed for collecting information about ventilation operation, quality, and performance throughout a mining operation. At each ventilation device, ventilation-related elements, such as fans, motors, belts, filters, temperature gauges, voltage, current, air quality, poison detection, and the like may be configured with a corresponding sensor. While variation in any one element (e.g., air volume per minute, and the like) may not be indicative of a problem, smart band analysis may be applied to detect trends over time that may be suggestive of potential problems with ventilation equipment. To perform smart bands analysis, data from a plurality of sensors may be required to form a basis for analysis. By implementing data collection systems for ventilation stations, data from a ventilation system may be captured. In an example, a smart band analysis may be indicated for a ventilation station. In response to this indication, a data collection system may be configured to collect data by routing data from sensors disposed at the ventilation station to a central monitoring facility that may gather and analyze data from several ventilation stations.
In embodiments, a system for data collection that applies smart band data collection templates to configure and utilize data collection and routing infrastructure may be applied to drive train data collection and analysis in mining environments. A drive train, such as a drive train for a mining vehicle may include a range of elements that could benefit from use of the methods and systems of data collection in an industrial environment as described herein. In particular, smart band-based data collection may be used to collect data from heavy duty mining vehicle drive trains under certain conditions that may be detectable by smart bands analysis. A smart bands-based data collection template may be used by a drivetrain data collection and routing system to configure sensors, data paths, and data collection resources to perform data collection under certain circumstances, such as those that may indicate an unacceptable trend of drive train performance. A data collection system for an industrial drive train may include sensing aspects of a non-steering axle, a planetary steering axle, drive shafts (e.g., main and wing shafts), transmissions, (e.g., standard, torque converters, long drop), and the like. A range of data related to these operational parts may be collected. However, data for support and structural members that support the drive train may also need to be collected for thorough smart band analysis. Therefore, collection across this wide range of drive train-related components may be triggered based on a smart band analysis determination of a need for this data. In an example, a smart band analysis may indicate potential slippage between a main and wing drive shaft that may represented by an increasing trend in response delay time of the wing drive shaft to main drive shaft operation. In response to this increasing trend, data collection modules disposed throughout the mining vehicle's drive train may be configured to route data from local sensors to be collected and analyzed by data collectors. Mining vehicle drive train smart based-based data collection may include a range of templates based on which type of trend is detected. If a trend related to a steering axle is detected, a data collection template to be implemented may be different in sensor content, duration, and the like than for a trend related to power demand for a normalized payload. Each template could configure data sensing, routing, and collection resources throughout the vehicle drive train accordingly.
Referring to
1. A system for data collection in an industrial environment comprising a data collection system that monitors at least one signal for a set of collection band parameters and upon detection of a parameter from the set of collection band parameters configures portions of the system and performs collection of data from a set of sensors based on the detected parameter.
2. The system of clause 1, wherein the at least one signal comprises an output of a sensor that senses a condition in the industrial environment.
3. The system of clause 1, wherein the set of collection band parameters comprises values derivable from the signal that are beyond an acceptable range of values derivable from the signal.
4. The system of clause 1, wherein configuring portions of the system comprises configuring a storage facility to accept data collected from the set of sensors.
5. The system of clause 1, wherein configuring portions of the system comprises configuring a data routing portion comprising at least one of an analog crosspoint switch, hierarchical multiplexer, analog to digital converter, intelligent sensor, and programmable logic component.
6. The system of clause 1, wherein detection of a parameter from the set of collection band parameters, comprises detecting a trend value for the signal being beyond an acceptable range of trend values.
7. The system of clause 1, wherein configuring portions of the system comprises implementing a smart band data collection template associated with the detected parameter.
8. A system for data collection in an industrial environment comprising a data collection system that monitors at least one signal for data values within a set of acceptable data values that represent acceptable collection band conditions for the signal and upon detection of a data value for the at least one signal outside of the set of acceptable data values, triggers a data collection activity that causes collecting data from a predetermined set of sensors associated with the monitored signal.
9. The system of clause 8, wherein the at least one signal comprises an output of a sensor that senses a condition in the industrial environment.
10. The system of clause 8, wherein the set of acceptable data value comprises values derivable from the signal that are within an acceptable range of values derivable from the signal.
11. The system of clause 8, further comprising configuring a storage facility of the system to facilitate collecting data from the predetermined set of sensors in response to the detection of a data value outside of the set of acceptable data values.
12. The system of clause 8, further comprising configuring a data routing portion of the system comprising at least one of an analog crosspoint switch, hierarchical multiplexer, analog to digital converter, intelligent sensor, and programmable logic component in response to the detection of a data value outside of the set of acceptable data values.
13. The system of clause 8, wherein detection of a data value for the at least one signal outside of the set of acceptable data values comprises detecting a trend value for the signal being beyond an acceptable range of trend values.
14. The system of clause 8, wherein the data collection activity is defined by a smart band data collection template associated with the detected parameter.
15. A method for data collection in an industrial environment comprising:
collection of data from one or more sensors configured to sense a condition of an industrial machine in the environment;
checking the collected data against a set of criteria that define an acceptable range of the condition; and in response to the collected data being violating the acceptable range of the condition, collecting data from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a smart band data collection template.
16. The method of clause 15, wherein violating the acceptable range of the condition comprises a trend of the data from the one or more sensors approaching a maximum value of the acceptable range.
17. The method of clause 15, wherein the smart-band group of sensors is defined by the smart band data collection template.
18. The method of clause 15, wherein the smart band data collection template comprises at least one of a list of sensors to activate, data from the sensors to collect, duration of collection of data from the sensors, and a destination location for storing the collected data.
19. The method of clause 15, wherein collecting data from a smart-band group of sensors comprises configuring at least one data routing resource of the industrial environment that facilitates routing data from the smart band group of sensors to a plurality of data collectors.
20. The method of clause 15, wherein the set of criteria comprises a range of trend values derived by processing the data from the one or more sensors.
21. A system for monitoring a ball screw actuator in an automated production environment comprising a data collection system that monitors at least one signal from the ball screw actuator for a set of collection band parameters and upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors disposed to monitor conditions of the ball screw actuator based on the detected parameter.
22. A system for monitoring a ventilation system in a mining environment comprising a data collection system that monitors at least one signal from the ventilation system for a set of collection band parameters and upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors disposed to monitor conditions of the ventilation system based on the detected parameter.
23. A system for monitoring a drive train of a mining vehicle comprising a data collection system that monitors at least one signal from the drive train for a set of collection band parameters and upon detection of a parameter from the set of collection band parameters, configures portions of the system and performs collection of data from a set of sensors disposed to monitor conditions of the drive train based on the detected parameter.
In embodiments, a system for data collection in an industrial environment may automatically configure local and remote data collection resources and may perform data collection from a plurality of system sensors that are identified as part of a group of sensors that produce data that is required to perform operational deflection shape rendering. In embodiments, the system sensors are distributed throughout structural portions of an industrial machine in the industrial environment. In embodiments, the system sensors sense a range of system conditions including vibration, rotation, balance, friction, and the like. In embodiments, automatically configuring is in response to a condition in the environment being detected outside of an acceptable range of condition values. In embodiments, a sensor in the identified group of system sensors senses the condition.
In embodiments, a system for data collection in an industrial environment may configure a data collection plan, such as a template to collect data from a plurality of system sensors distributed throughout a machine to facilitate automatically producing an operational deflection shape visualization based on machine structural information and a data set used to produce an operational deflection shape visualization of the machine.
In embodiments, a system for data collection in an industrial environment may configure a data collection template for collecting data in an industrial environment by identifying sensors disposed for sensing conditions of preselected structural members of an industrial machine in the environment based on an operational deflection shape visualization plan of the industrial machine. In embodiments, the template may include an order and timing of data collection from the identified sensors.
In embodiments, methods and systems for data collection in an industrial environment may include a method of establishing an acceptable range of sensor values for a plurality of industrial machine condition sensors by validating an operational deflection shape visualization of structural elements of the machine as exhibiting deflection within an acceptable range, wherein data from the plurality of sensors used in the validated operational deflection shape visualization define the acceptable range of sensor values.
In embodiments, a system for data collection in an industrial environment may include a plurality of data sources, such as sensors, that may be grouped for coordinated data collection to provide data required to produce an operational deflection shape visualization. Information regarding the sensors to group, data collection coordination requirements, and the like may be retrieved from an operation deflection shape data collection template. Coordinated data collection may include concurrent data collection. To facilitate concurrent data collection from a portion of the group of sensors, sensor routing resources of the system for data collection may be configured, such as by configuring a data multiplexer to route data from the portion of the group of sensors to which it connects to data collectors. In embodiments, each such source that connects an input of the multiplexer may be routed within the multiplexer to separate outputs so that data from all of the connected sources may be routed on to data collection elements of the industrial environment. In embodiments, the multiplexer may include data storage capabilities that may facilitate sharing a common output for at least a portion of the inputs. In embodiments, a multiplexer may include data storage capabilities and data bus-enabled outputs so that data for each source may be captured in a memory and transmitted over a data bus, such as a data bus that is common to the outputs of the multiplexer. In embodiments, sensors may be smart sensors that may include data storage capabilities and may send data from the data storage to the multiplexer in a coordinated manner that supports use of a common output of the multiplexer and/or use of a common data bus.
In embodiments, a system for data collection in an industrial environment may comprise templates for configuring the data collection system to collect data from a plurality of sensors to perform operational deflection shape visualization for a plurality of deflection shapes. Individual templates may be configured for visualization of looseness, soft joints, bending, twisting, and the like. Individual deflection shape data collection templates may be configured for different portions of a machine in an industrial environment.
In embodiments, a system for data collection in an industrial environment may facilitate operational deflection shape visualization that may include visualization of locations of sensors that contributed data to the visualization. In the visualization, each sensor that contributed data to generate the visualization may be indicated by a visual element. The visual element may facilitate user access to information about the sensor, such as its location, type, representative data contributed, path of data from the sensor to a data collector, a deflection shape template identifier, a configuration of a switch or multiplexer through which the data is routed, and the like. The visual element may be determined by associating sensor identification information received from a sensor with information, such as a sensor map, that correlates sensor identification information with physical location in the environment. The information may appear in the visualization in response to the visual element representing the sensor being selected, such as by a user positioning a cursor on the sensor visual element.
In embodiments, operation deflection shape visualization may benefit from data meeting a phase relationship requirement. A data collection system in the environment may be configured to facilitate collecting data that complies with the phase relationship requirement. Alternatively, the data collection system may be configured to collect data from a plurality of sensors that contains data that meets the phase relationship requirements but may also include data that does not. A post processing operation that may access phase detection data may select a subset of the collected data.
In embodiments, a system for data collection in an industrial environment may include a multiplexer receiving data from a plurality of sensors and multiplexing the received data for delivery to a data collector. The data collector may process the data to facilitate operational deflection shape visualization Operational deflection shape visualization may require data from several different sensors and may benefit from using a reference signal, such as data from a sensor when processing data from the different sensors. The multiplexer may be configured to provide data from the different sensors, such as by switching among its inputs over time so that data from each sensor may be received by the data collector. However, the multiplexer may include a plurality of outputs so that at least a portion of the inputs may be routed to least two of the plurality of outputs. Therefore, in embodiments, a multiple output multiplexer may be configured to facilitate data collection that may be suitable for operational deflection shape visualization by routing a reference signal from one of its inputs (e.g., data from an accelerometer) to one of its outputs and multiplexing data from a plurality of its outputs onto one or more of its outputs while maintaining the reference signal output routing. A data collector may collect the data from the reference output and use that to align the multiplexed data from the other sensors.
In embodiments, as depicted in
In embodiments a system for data collection in an industrial environment may facilitate operational deflection shape visualization 7014 through coordinated data collection related to conveyors for mining applications. Mining operations may rely on conveyor systems to move material, supplies, and equipment into and out of a mine. Mining operations may typically operate around the clock; therefore, conveyor downtime may have a substantive impact on productivity and costs. Advanced analysis of conveyor and related systems that focuses on secondary affects that may be challenging to detect merely through point observation may be more readily detected via operational deflection shape visualization (ODSV). Capturing operational data related to vibration, stresses and the like can facilitate ODSV. However, data coordination of data capture provides more reliable results. Therefore, a data collection system that may have sensors dispersed throughout a conveyor system can be configured to facilitate such coordinated data collection. In an example, capture of data affecting structural components of a conveyor, such as landing points and the horizontal members that connect them and support the conveyer between landing points, conveyer segment handoff points, motor mounts, mounts of conveyer rollers, and the like may need to be coordinated with data related to conveyor dynamic loading, drive systems, motors, gates, and the like. A system for data collection in an industrial environment, such as a mining environment may include data sensing and collection modules placed throughout the conveyor at locations such as segment handoff points, drive systems, and the like. Each module may be configured by one or more controllers, such as programmable logic controllers that may be connected through a physical or logical (e.g., wireless) communication bus that aids in performing coordinated data collection. To facilitate coordination, a reference signal, such as a trigger and the like may be communicated among the modules for use when collecting data. In embodiments, data collection and storage may be performed at each module so as to reduce the need for real-time transfer of sensed data throughout the mining environment. Transfer of data from the modules to an ODSV processing facility may be performed after collection or as communication bandwidth between the module sand the processing facility allows. ODSV can provide insight into conditions in the conveyer, such as deflection of structural members that may, over time cause premature failure. Coordinated data collection with a data collection system for use in an industrial environment, such as mining can enable ODSV that may reduce operating costs by reducing down time due to unexpected component failure.
In embodiments, a system for data collection in an industrial environment may facilitate operational deflection shape visualization through coordinated data collection related to fans for mining applications. Fans provide a crucial function in mining operations of moving air throughout a mine to provide ventilation, equipment cooling, combustion exhaust evacuation, and the like. Ensuring reliable and often continuous operation of fans may be critical for miner safety and cost-effective operations. Dozens or hundreds of fans may be used in large mining operations. Fans, such as fans for ventilation management may include circuit, booster and auxiliary types. High capacity auxiliary fans may operate at high rates of speed, over 2500 revolutions per minute (RPM). Performing operation deflection shape visualization (ODSV) may reveal important reliability information about fans deployed in a mining environment. Collecting the range of data needed for ODSV of mining fans may be performed by a system for collecting data in industrial environments as described herein. In embodiments, sensing elements, such as intelligent sensing and data collection modules may be deployed with fans and/or fan subsystems. These modules may exchange collection control information (e.g., over a dedicated control bus and the like) so that data collection may be coordinated in time and phase to facilitate ODSV.
A large auxiliary fan for use in mining may be constructed for transportability into and through the mine and therefore may include a fan body, intake and outlet ports, dilution valves, protection cage, electrical enclosure, wheels, access panels, and other structural and/or operational elements. OSDV of such an auxiliary fan may require collection of data from many different elements. A system for data collection may be configured to sense and collect data that may be combined with structural engineering data to facilitate ODSV for this type of industrial fan.
Referring to
1. A method of data collection for performing operational deflection shape visualization in an industrial environment comprising:
automatically configuring local and remote data collection resources; and
collecting data from a plurality of sensors using the configured resources, wherein the plurality of sensors comprise a group of sensors that produce data that is required to perform the operational deflection shape visualization
2. The method of clause 1, wherein the sensors are distributed throughout structural portions of an industrial machine in the industrial environment.
3. The method of clause 1, wherein the sensors sense a range of system conditions including vibration, rotation, balance, and friction.
4. The method of clause 1, wherein the automatically configuring is in response to a condition in the environment being detected outside of an acceptable range of condition values.
5. The method of clause 4, wherein the condition is sensed by a sensor in the group of system sensors.
6. The method of clause 1, wherein automatically configuring comprises configuring a signal switching resource to concurrently connect a portion of the group of sensors to data collection resources.
7. The method of clause 6, wherein the signal switching resource is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform operational deflection shape visualization
8. A method of data collection in an industrial environment, comprising:
configuring a data collection plan to collect data from a plurality of system sensors distributed throughout a machine in the industrial environment, the plan based on machine structural information and an indication of data needed to produce an operational deflection shape visualization of the machine;
configuring data sensing, routing and collection resources in the environment based on the data collection plan; and collecting data based on the data collection plan.
9. The method of clause 8, further comprising producing the operational deflection shape visualization based on the collected data.
10. The method of clause 8, wherein the configuring data sensing, routing and collection resources is in response to a condition in the environment being detected outside of an acceptable range of condition values.
11. The method of clause 10, wherein the condition is sensed by a sensor identified in the data collection plan.
12. The method of clause 8, wherein configuring data sensing, routing, and collection resources comprises configuring a signal switching resource to concurrently connect the plurality of system sensors to data collection resources.
13. The method of clause 12, wherein the signal switching resource is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform operational deflection shape visualization
14. A system for data collection in an industrial environment comprising:
a plurality of sensors disposed throughout the environment;
a multiplexer that connects signals from the plurality of sensors to data collection resources;
a programmable logic component configured to control the sensors and the multiplexer;
an operational deflection shape visualization data collection template that identifies sensors, multiplexer configuration, and programmable logic component control parameters for collection of data for performing operational deflection shape visualization; and
a processor for processing data collected from the plurality of sensors in response to the data collection template, the processing resulting in an operational deflection shape visualization of a portion of a machine disposed in the environment.
15. The system of clause 14, wherein operational deflection shape data collection template further identifies a condition in the environment that triggers performing data collection from the identified sensors.
16. The system of clause 15, wherein the condition is sensed by a sensor identified in the operational deflection shape visualization data collection template.
17. The system of clause 14, wherein the operational deflection shape visualization data collection template specified inputs of the multiplexer to concurrently connect to data collection resources.
18. The system of clause 17, wherein the multiplexer is configured to maintain a connection between a reference sensor and the data collection resources throughout a period of collecting data from the sensors to perform operational deflection shape visualization
19. The system of clause 14, wherein the operational deflection shape visualization data collection template specifies data collection requirements for performing operational deflection shape visualization for at least one of looseness, soft joints, bending, and twisting of a portion of a machine in the industrial environment.
20. The system of clause 14, wherein the operational deflection shape visualization data collection template specifies an order and timing of data collection from a plurality of identified sensors.
21. A method of monitoring a mining conveyer for performing operational deflection shape visualization of the conveyer comprising:
automatically configuring local and remote data collection resources; and
collecting data from a plurality of sensors disposed to sense the mining conveyor using the configured resources, wherein the plurality of sensors comprise a group of sensors that produce data that is required to perform the operational deflection shape visualization of a portion of the conveyor.
22. A method of monitoring a mining fan for performing operational deflection shape visualization of the fan comprising:
automatically configuring local and remote data collection resources; and
collecting data from a plurality of sensors disposed to sense the fan using the configured resources, wherein the plurality of sensors comprise a group of sensors that produce data that is required to perform the operational deflection shape visualization of a portion of the fan.
In embodiments, a system for data collection in an industrial environment may include a hierarchical multiplexer that facilitates successive multiplexing of input data channels according to a configurable hierarchy, such as a user configurable hierarchy. The system for data collection in an industrial environment may include the hierarchical multiplexer that facilitates successive multiplexing of a plurality of input data channels according to a configurable hierarchy. The hierarchy may be automatically configured by a controller based on an operational parameter in the industrial environment, such as a parameter of a machine in the industrial environment.
In embodiments, a system for data collection in an industrial environment may include a plurality of sensors that may output data at different rates. The system may also include a multiplexer module that receives sensor outputs from a first portion of the plurality of sensors with similar output rates into separate inputs of a first hierarchical multiplexer of the multiplexer module that provides at least one multiplexed output of a portion of the its inputs to a second hierarchical multiplexer that receives sensor outputs from a second portion of the plurality of sensors with similar output rates and that provides at least one multiplexed output of a portion of its inputs. In embodiments, the output rates of the first set of sensors is slower than the output rate of the second set of sensors. In embodiments, data collection rate requirements of the first set of sensors is lower than the data collection rate requirements of the second set of sensors. In embodiments, the first hierarchical multiplexer output is a time-multiplexed combination of a portion of its inputs. In embodiments, the second multiplexer receives sensor signals with output rates that are similar to a rate of output of the first multiplexer, wherein the first multiplexer produces time-based multiplexing of the portion of its plurality of inputs.
In embodiments, a system for data collection in an industrial environment may include a hierarchical multiplexer that is dynamically configured based on a data acquisition template. The hierarchical multiplexer may include a plurality of inputs and a plurality of outputs, wherein any input can be directed to any output in response to sensor output collection requirements of the template, and wherein a subset of the inputs can be multiplexed at a first switching rate and output to at least one of the plurality of outputs.
In embodiments, a system for data collection in an industrial environment may include a plurality of sensors for sensing conditions of a machine in the environment, a hierarchical multiplexer, a plurality of Analog to Digital Converters (ADCs), a processor, local storage, and an external interface. The system may use the processor to access a data acquisition template of parameters for data collection from a portion of the plurality of sensors, configure the hierarchical multiplexer, the ADCs and the local storage to facilitate data collection based on the defined parameters, and execute the data collection with the configured elements including storing a set of data collected from a portion of the plurality of sensors into the local storage. In embodiments, the ADCs convert analog sensor data into a digital form that is compatible with the hierarchical multiplexer. In embodiments, the processor monitors at least one signal generated by the sensors for a trigger condition and upon detection of the trigger condition responds by at least one of communicating an alert over the external interface and performing data acquisition according to a template that corresponds to the trigger condition.
In embodiments, a system for data collection in an industrial environment may include a hierarchical multiplexer that may be configurable based on a data collection template of the environment. The multiplexer may support receiving a large number of data signals (e.g., from sensors in the environment) simultaneously. In embodiments, all sensors for a portion of an industrial machine in the environment may be individually connected to inputs of a first stage of the multiplexer. The first stage of the multiplexer may provide a plurality of outputs that may feed into a second multiplexer stage. The second state multiplexer may provide multiple outputs that feed into a third stage, and so on. Data collection templates for the environment may be configured for certain data collection sets, such as a set to determine temperature throughout a machine or a set to determine vibration throughout a machine, and the like. Each template may identify a plurality of sensors in the environment from which data is to be collected, such as during a data collection event. When a template is presented to the hierarchical multiplexer, mapping of inputs to outputs for each multiplexing stage may be configured so that the required data is available at output(s) of a final multiplexing hierarchical stage for data collection. In an example, a data collection template to collect a set of data to determine temperature throughout a machine in the environment may identify many temperature sensors. The first stage multiplexer may respond to the template by selecting all of the available inputs that connect to temperature sensors. The data from these sensors maybe multiplexed onto multiple inputs of a second stage sensor that may perform time-based multiplexing to produce a time-multiplexed output(s) of temperature data from a portion of the sensors. These outputs may be gathered by a data collector and de-multiplexed into individual sensor temperature readings.
In embodiments, time sensitive signals, such as triggers and the like may connect to inputs that directly connect to a final multiplexer stage, thereby reducing any potential delay caused by routing through multiple multiplexing stages.
In embodiments, a hierarchical multiplexer in a system for data collection in an industrial environment may comprise an array of relays, a programmable logic component, such as a CPLD, a field programmable gate array (FPGA), and the like.
In embodiments, a system for data collection in an industrial environment that may include a hierarchical multiplexer for routing sensor outputs onto signal paths may be used with explosive systems in mining applications. Blast initiating and electronic blasting systems provide for computer assisted blasting. Ensuring that blasting occurs safely may involve effective sensing and analysis of a range of conditions. A system for data collection in an industrial environment may be deployed to sense and collect data associated with explosive systems, such as explosive systems used for mining A data collection system can use a hierarchical multiplexer to capture data from explosive system installations automatically by aligning a deployment of an explosive system with the hierarchical multiplexer. An explosive system may be deployed with a form of hierarchy that starts with a primary initiator and follows detonation connections through successive layers of electronic blast control to sequenced detonation. Data collected from each of these layers of blast systems configuration may be associated with stages of a hierarchical multiplexer so that data collected from bulk explosive detonation can be captured in a hierarchy that corresponds to its blast control hierarchy.
In embodiments, a system for data collection in an industrial environment that may include a hierarchical multiplexer for routing sensor outputs onto signal paths may be used with refinery blowers in oil and gas pipeline applications. Refinery blower applications include fired heater combustion air preheat systems and the like. Forced draft blowers may include a range of moving and moveable parts that may benefit from condition sensing and monitoring. Sensing may include detecting conditions of couplings (e.g., temperature, rotational rate, and the like), motor (vibration, temperature, RPMs, torque, power usage, and the like), louver mechanics (actuators, louvers, and the like), plenum (flow rate, blockage, back pressure, and the like). A system for data collection in an industrial environment that uses a hierarchical multiplexer for routing signals from sensors and the like to data collectors may be configured to collect data from a refinery blower. In an example, a plurality of sensors may be deployed to sense air flow into, throughout, and out of a forced draft blower used in a refinery application, such as to preheat combustion air. Sensors may be grouped based on a frequency of a signal produced by sensors. Sensors that detect louver position and control may produce data at a lower rate than sensors that detect blower RPMs. Therefore, louver position and control sensor signals can be applied to a lower stage in a multiplexer hierarchy than the blower RPM sensors because data from louvers change less often than data from RPM sensor. A data collection system could switch among a plurality of louver sensors and still capture enough information to properly detect louver position; however, properly detecting blower RPM may require greater bandwidth of connection between the blower RPM sensor and a data collector. A hierarchical multiplexer may enable capturing blower RPM data at a rate that is required for proper detection (perhaps by outputting the RPM sensor data for long durations of time), while switching among several louver sensor inputs and directing them onto an output that is different than the blower RPM output. Alternatively, the louver inputs may be time multiplexed with the blower RPM data onto a single output that can be de-multiplexed by a data collector that is configured to determine when blower RPM data is being output and when louver position data is being output.
In embodiments, a system for data collection in an industrial environment that may include a hierarchical multiplexer for routing sensor outputs onto signal paths may be used with pipeline related compressors (e.g., reciprocating) in oil and gas pipeline applications. A typical use of a reciprocating compressor for pipeline application is production of compressed air for pipeline testing. A system for data collection in an industrial environment may apply a hierarchical multiplexer while collecting data from a pipeline testing-based reciprocating compressor. Sensors deployed along a portion of a pipeline being tested may be input to the lowest stage of the hierarchical multiplexer because these sensors may be periodically sampled prior to and during testing; however, the rate of sampling may be low relative to sensors that detect compressor operation, such as parts of the compressor that operate at higher frequencies, such as the reciprocating linkage, motor, and the like. The sensors that provide data at frequencies that enable reproduction of the detect motion may be input to higher stages in the hierarchical multiplexer. Time multiplexing among the pipeline sensors may provide for coverage of a large number of sensors while capturing events, such as seal leakage and the like. However, time multiplexing among reciprocating linkage sensors may require output signal bandwidth that may exceed the bandwidth available for routing data from the multiplexer to a data collector. Therefore, in embodiments, a plurality of pipeline sensors may be time-multiplexed onto a single multiplexer output and a compressor sensor detecting rapidly moving parts, such as the compressor motor, may be routed to separate outputs of the multiplexer.
Referring to
a controller for controlling data collection resources in the industrial environment; and
a hierarchical multiplexer that facilitates successive multiplexing of a plurality of input data channels according to a configurable hierarchy, wherein the hierarchy is automatically configured by the controller based on an operational parameter of a machine in the industrial environment.
2. The system of clause 1, wherein the operational parameter of the machine is identified in a data collection template.
3. The system of clause 1, wherein the hierarchy is automatically configured in response to smart band data collection activation.
4. The system of clause 1, further comprising an analog to digital converter disposed between a source of the input data channels and the hierarchical multiplexer.
5. The system of clause 1, wherein the operational parameter of the machine comprises a trigger condition of at least one of the data channels.
6 A system for data collection in an industrial environment comprising:
a plurality of sensors; and
a multiplexer module comprising a first hierarchical multiplexer and a second hierarchical multiplexer and which receives sensor output signals from a first portion of the plurality of sensors with similar output rates into separate inputs of the first hierarchical multiplexer that provides at least one multiplexed output signal of a portion of its inputs to the second hierarchical multiplexer, with the second hierarchical multiplexer receiving sensor output signals from a second portion of the plurality of sensors and providing at least one multiplexed output signal of a portion of its inputs.
7. The system of clause 6, wherein the second portion of the plurality of sensors output data at rates that are higher than the output rates of the first portion of the plurality of sensors.
8. The system of clause 6, wherein the first portion and the second portion of the plurality of sensors output data at different rates.
9. The system of clause 6, wherein the first hierarchical multiplexer output is a time-multiplexed combination of a portion of its inputs.
10. The system of clause 6, wherein the second multiplexer receives sensor signals with output rates that are similar to a rate of output of the first multiplexer, and wherein the first multiplexer produces time-based multiplexing of the portion of its plurality of inputs.
11. A system for data collection in an industrial environment comprising:
a plurality of sensors for sensing conditions of a machine in the environment;
a hierarchical multiplexer;
a plurality of Analog to Digital Converters (ADCs);
a controller;
local storage; and
an external interface, the system using the controller to access a data acquisition template that defines parameters for data collection from a portion of the plurality of sensors, configure the hierarchical multiplexer, the ADCs, and the local storage to facilitate data collection based on the defined parameters, and execute the data collection with the configured elements including storing a set of data collected from a portion of the plurality of sensors into the local storage.
12. The system of clause 11, wherein the ADCs converts analog sensor data into a digital form that is compatible with the hierarchical multiplexer.
13. The system of clause 11, wherein the processor monitors at least one signal generated by the sensors for a trigger condition and upon detection of the trigger condition responds by at least one of communicating an alert over the external interface and performing data acquisition according to a template that corresponds to the trigger condition.
14. The system of clause 11, wherein the hierarchical multiplexer performs successive multiplexing of data received from the plurality of sensors according to a configurable hierarchy, wherein the hierarchy is automatically configured by the controller based on an operational parameter of a machine in the industrial environment.
15. The system of clause 14, wherein the operational parameter of the machine is identified in a data collection template.
16. The system of clause 14, wherein the hierarchy is automatically configured in response to smart band data collection activation.
17. The system of clause 14, further comprising an analog to digital converter disposed between a source of the input data channels and the hierarchical multiplexer.
18. The system of clause 14, wherein the operational parameter of the machine comprises a trigger condition of at least one of the data channels.
19. The system of clause 11, wherein the hierarchical multiplexer performs successive multiplexing of data received from the plurality of sensors according to a configurable hierarchy, wherein the hierarchy is automatically configured by a controller based on a detected parameter of an industrial environment.
20. The system of clause 19, wherein the parameter of the industrial environment comprises a trigger condition of at least one of the data channels.
21. A system for monitoring a mining explosive subsystem comprising:
a controller for controlling data collection resources associated with the mining explosive subsystem; and
a hierarchical multiplexer that facilitates successive multiplexing of a plurality of input data channels according to a configurable hierarchy, wherein the hierarchy is automatically configured by the controller based on a configuration of the mining explosive subsystem.
22. A system for monitoring a refinery blower in an oil and gas pipeline applications comprising:
a controller for controlling data collection resources associated with the refinery blower; and
a hierarchical multiplexer that facilitates successive multiplexing of a plurality of input data channels according to a configurable hierarchy, wherein the hierarchy is automatically configured by the controller based on a configuration of the refinery blower.
23. A system for monitoring a reciprocating compressor in an oil and gas pipeline applications comprising:
a controller for controlling data collection resources associated with the reciprocating compressor; and
a hierarchical multiplexer that facilitates successive multiplexing of a plurality of input data channels according to a configurable hierarchy, wherein the hierarchy is automatically configured by the controller based on a configuration of the reciprocating compressor.
In embodiments, a system for data collection in an industrial environment may include an ultrasonic sensor disposed to capture ultrasonic conditions of an element of in the environment. The system may be configured to collect data representing the captured ultrasonic condition in a computer memory, on which a processor may execute an ultrasonic analysis algorithm. In embodiments, the sensed element may be one of a moving element, a rotating element, a structural element and the like. In embodiments, the data may be streamed to the computer memory. In embodiments, the data may be continuously streamed. In embodiments, the data may be streamed for a duration of time, such as an ultrasonic condition sampling duration. In embodiments, the system may also include a data routing infrastructure that facilitates routing the streaming data from the ultrasonic sensor to a plurality of destinations including local and remote destinations. The routing infrastructure may include a hierarchical multiplexer that is adapted to route the streaming data and data from at least one other sensor to a destination.
In embodiments, ultrasonic monitoring in an industrial environment may be performed by a system for data collection as described herein on rotating elements (e.g., motor shafts and the like), bearings, fittings, couplings, housings, load bearing elements, and the like. The ultrasonic data may be used for pattern recognition, state determination, time-series analysis and the like, any of which may be performed by computing resources of the industrial environment, which may include local computing resources (e.g., resources located within the environment and/or within a machine in the environment, and the like) and remote computing resources (e.g., cloud-based computing resources, and the like).
In embodiments, ultrasonic monitoring in an industrial environment by a system for data collection may be activated in response to a trigger (e.g., a signal from a motor indicating the motor is operational, and the like), a measure of time (e.g., an amount of time since the most recent monitoring activity, a time of day, a time relative to a trigger, an amount of time until a future event, such as machine shutdown, and the like), an external event (e.g., lightning strike, and the like). The ultrasonic monitoring may be activated in response to implementation of a smart band data collection activity. The ultrasonic monitoring may be activated in response to a data collection template being applied in the industrial environment. The data collection template may be configured based on analysis of prior vibration-caused failures that may be applicable to the monitored element, machine, environment and the like. Because continuous monitoring of ultrasonic data may require dedicating data routing resources in the industrial environment for extended periods of time, a data collection template for continuous ultrasonic monitoring may be configured with data routing and resource utilization setup information that a controller of a data collection system may use to setup the resources to accommodate continuous ultrasonic monitoring. In an example, a data multiplexer may be configured to dedicate a portion of its outputs to the ultrasonic data for a duration of time specified in the template.
In embodiments, a system for data collection in an industrial environment may perform continuous ultrasonic monitoring. The system may also include processing of the ultrasonic data by a local processor located proximal to the vibration monitoring sensor or device(s). Depending on the computing capabilities of the local processor, functions such as peak detection may be performed. A programmable logic component may provide sufficient computing capabilities to perform peak detection. Processing of the ultrasonic data (local or remote) may provide feedback to a controller associated with the element(s) being monitored. The feedback may be used in a control look to potentially adjust an operating condition, such as rotational speed, and the like, in an attempt to reduce or at least contain potential negative impact suggested by the ultrasonic data analysis.
In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring, and in particular continuous ultrasonic monitoring. The ultrasonic monitoring data may be combined with multi-dimensional models of an element or machine being monitored to produce a visualization of the ultrasonic data. In embodiments an image, set of images, video, and the like may be produced that correlates in time with the sensed ultrasonic data. In embodiments, image recognition and/or analysis may be applied to ultrasonic visualizations to further facilitate determine of a severity of a condition detected by the ultrasonic monitoring. The image analysis algorithms may be trained to detect normal and out of bounds conditions. Data from load sensors may be combined with ultrasonic data to facilitate testing materials and systems.
In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring of a pipeline in an oil and gas pipeline application. Flows of petroleum through pipelines can create vibration and other mechanical effects that may contribute to structural changes in a liner of the pipeline, support members, flow boosters, regulators, diverters, and the like. Performing continuous ultrasonic monitoring of key elements in a pipeline may facilitate detection in early changes in material, such as joint fracturing and the like that may lead to failure. A system for data collection in an industrial environment may be configured with ultrasonic sensing devices that may be connected through signal data routing resources, such as crosspoint switches, multiplexers, and the like to data collection and analysis nodes at which the collected ultrasonic data can be collected and analyzed. In embodiments, a data collection system may include a controller that may reference a data collection plan or template that includes information to facilitate configuring the data sampling, routing and collection resources of the system to accommodate collection of ultrasonic sample data from a plurality of elements along the pipeline. The template may indicate a sequence for collecting ultrasonic data from a plurality of ultrasonic sensors and the controller may configure a multiplexer to route ultrasonic sensor data from a specified ultrasonic sensor to a destination, such as a data storage controller, analysis processor and the like, for a duration specified in the template. The controller may detect a sequence of collection in the template, or a sequence of templates to access, and respond to each template in the detected sequence, adjusting the multiplexer and the like to route the sensor data specified in each template to a collector.
In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring of compressors in a power generation application. Compressors include several critical rotating elements, such as (e.g., shaft, motor, and the like), rotational support elements (bearings, couplings and the like), and the like. A system for data collection configured to facilitate sensing, routing, collection and analysis of ultrasonic data in a power generation application may receive ultrasonic sensor data from a plurality of ultrasonic sensors. Based on a configuration setup template, such as a template for collecting continuous ultrasonic data from one or more ultrasonic sensor devices, a controller may configure resources of the data collection system to facilitate delivery of the ultrasonic data over one or more signal data likes from the sensor(s) at least to data collectors, that may be locally or remotely accessible. In embodiments, a template may indicate that ultrasonic data for a main shaft should be retrieved continuously for one minute, and then ultrasonic data for a secondary shaft should be retrieved for another minute, followed by ultrasonic data for a housing of the compressor. The controller may configure a multiplexer that receives the ultrasonic data for each of these sensors to route the data from each sensor in order by configuring a control set that initially directs the inputs from the main shaft ultrasonic sensors through the multiplexer until the time or other measure of data being forwarded is reached. The controller could switch the multiplexer to route the additional ultrasonic data as required to satisfy the second template requirements. The controller may continue adjusting the data collection system resources along the way until all of the ultrasonic monitoring data collection templates are satisfied.
In embodiments, a system for data collection in an industrial environment may perform ultrasonic monitoring of wind turbine gearboxes in a wind energy generation application. Gearboxes in wind turbines may experience a high degree of resistance in operation due in part to the changing nature of wind, which may cause moving parts, such as the gear planes, hydraulic fluid pumps, regulators, and the like to prematurely fail. A system for data collection in an industrial environment may be configured with ultrasonic sensors that capture information that may lead to early detection of potential failure modes of these high-strain elements. To ensure that ultrasonic data may effectively be acquired from several different ultrasonic sensors with sufficient coverage to facilitate producing an actionable ultrasonic imaging assessment, the system may be configured specifically to deliver sufficient data at a relatively high rate from one or more of the sensors. Routing channel(s) may be dedicated to transfer of ultrasonic sensing data for a duration of time that may be specified in an ultrasonic data collection plan or template. To accomplish this, a controller, such as a programmable logic component, may configure a portion of a crosspoint switch and data collectors to deliver ultrasonic data from a first set of ultrasonic sensors (e.g., those that sense hydraulic fluid flow control elements) to a plurality of data collectors. Another portion of the crosspoint switch may be configured to route additional sensor data that may be useful for evaluating the ultrasonic data (e.g., motor on/off state, thermal condition of sensed parts, and the like) on other data channels to data collectors where the data can be combined and analyzed. The controller may reconfigure the data routing resources to enable collecting ultrasonic data from other elements based on a corresponding data collection template.
Referring to
1. A system for data collection in an industrial environment comprising:
an ultrasonic sensor disposed to capture ultrasonic conditions of a element of in the environment;
a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template; and
a processor executing an ultrasonic analysis algorithm on the data after arrival at the destination
2. The system of clause 1, wherein the template defines a time interval of continuous ultrasonic data capture from the ultrasonic sensor.
3. The system of clause 1, further comprising a data routing infrastructure that facilitates routing the streaming data from the ultrasonic sensor to a plurality of destinations including local and remote destinations, the routing infrastructure comprising a hierarchical multiplexer that is adapted to route the streaming data and data from at least one other sensor to a destination.
4. The system of clause 1, wherein the element in the environment is selected from the list consisting of rotating elements, bearings, fittings, couplings, housing, and load bearing parts.
5. The system of clause 1, wherein the template defines a condition of activation of continuous ultrasonic monitoring.
6. The system of clause 5, wherein the condition of activation is selected from a list consisting of a trigger, a smart-band, a template, an external event, regulatory compliance.
7. A system for data collection in an industrial environment comprising:
an ultrasonic sensor disposed to capture ultrasonic conditions of a element of in an industrial machine in the environment;
a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template; and
a processor executing an ultrasonic analysis algorithm on the data after arrival at the destination
8. The system of clause 7, wherein the template defines a time interval of continuous ultrasonic data capture from the ultrasonic sensor.
9. The system of clause 7, further comprising a data routing infrastructure that facilitates routing the data from the ultrasonic sensor to a plurality of destinations including local and remote destinations, the routing infrastructure comprising a hierarchical multiplexer that is adapted to route the ultrasonic data and data from at least one other sensor to a destination.
10. The system of clause 7, wherein the element in industrial machine is selected from the list consisting of rotating elements, bearings, fittings, couplings, housing, and load bearing parts.
11. The system of clause 7, wherein the template defines a condition of activation of continuous ultrasonic monitoring.
12. The system of clause 11, wherein the condition of activation is selected from a list consisting of a trigger, a smart-band, a template, an external event, regulatory compliance.
13. A method of continuous ultrasonic monitoring in an industrial environment comprising:
disposing an ultrasonic monitoring device within ultrasonic monitoring range of at least one moving part of an industrial machine in the industrial environment, the ultrasonic monitoring device producing a stream of ultrasonic monitoring data;
configuring, based on an ultrasonic monitoring data collection template a data routing infrastructure to route the stream of ultrasonic monitoring data to a destination, wherein the infrastructure facilitates routing data from a plurality of sensors through at least one of an analog cross-point switch and a hierarchical multiplexer to a plurality of destinations; routing the ultrasonic monitoring device data through the routing infrastructure to a destination;
storing the data in a computer accessible memory at the destination; and processing the stored data with an ultrasonic data analysis algorithm that provides an ultrasonic analysis of at least one of a motor shaft, bearings, fittings, couplings, housing, and load bearing parts.
14. The method of clause 13, wherein the data collection template defines a time interval of continuous ultrasonic data capture from the ultrasonic monitoring device.
15. The method of clause 13, wherein configuring the data routing infrastructure comprises configuring the hierarchical multiplexer to route the ultrasonic data and data from at least one other sensor to a destination
16. The method of clause 13, wherein ultrasonic monitoring is performed on at least one element in industrial machine that is selected from the list consisting of rotating elements, bearings, fittings, couplings, housing, and load bearing parts.
17. The method of clause 13, wherein the template defines a condition of activation of continuous ultrasonic monitoring.
18. The method of clause 17, wherein the condition of activation is selected from a list consisting of a trigger, a smart-band, a template, an external event, regulatory compliance.
19. The method of clause 13, wherein the ultrasonic data analysis algorithm performs pattern recognition.
20. The method of clause 13, wherein routing the ultrasonic monitoring device data is in response to detection of a condition in the industrial environment associated with the at least one moving part.
21. A system for monitoring an oil or gas pipeline comprising:
an ultrasonic sensor disposed to capture ultrasonic conditions of the pipeline;
a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template; and
a processor executing an ultrasonic analysis algorithm on the pipeline data after arrival at the destination
22. A system for monitoring a power generation compressor comprising:
an ultrasonic sensor disposed to capture ultrasonic conditions of the power generation compressor;
a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template; and
a processor executing an ultrasonic analysis algorithm on the power generation compressor data after arrival at the destination
23. A system for monitoring wind turbine gearbox comprising:
an ultrasonic sensor disposed to capture ultrasonic conditions of the gearbox;
a controller that configures data routing resources of the data collection system to route ultrasonic data being captured by the ultrasonic sensor to a destination location that is specified by an ultrasonic monitoring data collection template; and
a processor executing an ultrasonic analysis algorithm on the gearbox data after arrival at the destination.
Referring to
In embodiments, the foregoing neural network may be configured to connect with a DAQ instrument and other data collectors that may receive analog signals from one or more sensors. The foregoing neural networks may also be configured to interface with, connect to, or integrate with expert systems that can be local and/or available through one or more cloud networks. In embodiments,
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In many embodiments, an expert system or neural network may be trained, such as by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets that represent values, such as sensor data, event data, parameter data, and other types of data (including the many types described throughout this disclosure), as well as one or more indicators of an outcome, such as an outcome of a process, an outcome of a calculation, an outcome of an event, an outcome of an activity, or the like. Training may include training in optimization, such as training a neural network to optimize one or more systems based on one or more optimization approaches, such as Bayesian approaches, parametric Bayes classifier approaches, k-nearest-neighbor classifier approaches, iterative approaches, interpolation approaches, Pareto optimization approaches, algorithmic approaches, and the like. Feedback may be provided in a process of variation and selection, such as with a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds
In embodiments, a plurality of neural networks may be deployed in a cloud platform that receives data streams and other inputs collected (such as by mobile data collectors) in one or more industrial environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission. In the cloud platform, optionally using massively parallel computational capability, a plurality of different neural networks of several types (including modular forms, structure-adaptive forms, hybrids, and the like) may be used to undertake prediction, classification, control functions, and provide other outputs as described in connection with expert systems disclosed throughout this disclosure. The different neural networks may be structured to compete with each other (optionally including use evolutionary algorithms, genetic algorithms, or the like), such that an appropriate type of neural network, with appropriate input sets, weights, node types and functions, and the like, may be selected, such as by an expert system, for a specific task involved in a given context, workflow, environment process, system, or the like.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed forward neural network, which moves information in one direction, such as from a data input, like an analog sensor located on or proximal to an industrial machine, through a series of neurons or nodes, to an output. Data may move from the input nodes to the output nodes, optionally passing through one or more hidden nodes, without loops. In embodiments, feed forward neural networks may be constructed with various types of units, such as binary McCulloch-Pitts neurons, the simplest of which is a perceptron.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions. In embodiments, each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, such as one that employs a distance criterion with respect to a center (e.g., a Gaussian function). A radial basis function may be applied as a replacement for a hidden layer, such as a sigmoidal hidden layer transfer, in a multi-layer perceptron. An RBF network may have two layers, such as where an input is mapped onto each RBF in a hidden layer. In embodiments, an output layer may comprise a linear combination of hidden layer values representing, for example, a mean predicted output. The output layer value may provide an output that is the same as or similar to that of a regression model in statistics. In classification problems, the output layer may be a sigmoid function of a linear combination of hidden layer values, representing a posterior probability. Performance in both cases is often improved by shrinkage techniques, such as ridge regression in classical statistics. This corresponds to a prior belief in small parameter values (and therefore smooth output functions) in a Bayesian framework. RBF networks may avoid local minima, because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. In regression problems, this can be found in one matrix operation. In classification problems, the fixed non-linearity introduced by the sigmoid output function may be handled using an iteratively re-weighted least squares function or the like.
RBF networks may use kernel methods such as support vector machines (SVM) and Gaussian processes (where the RBF is the kernel function). A non-linear kernel function may be used to project the input data into a space where the learning problem can be solved using a linear model.
In embodiments, an RBF neural network may include an input layer, a hidden layer and a summation layer. In the input layer, one neuron appears in the input layer for each predictor variable. In the case of categorical variables, N−1 neurons are used, where N is the number of categories. The input neurons may, in embodiments, standardize the value ranges by subtracting the median and dividing by the interquartile range. The input neurons may then feed the values to each of the neurons in the hidden layer. In the hidden layer, a variable number of neurons may be used (determined by the training process). Each neuron may consist of a radial basis function that is centered on a point with as many dimensions as a number of predictor variables. The spread (e.g., radius) of the RBF function may be different for each dimension. The centers and spreads may be determined by training. When presented with the vector of input values from the input layer, a hidden neuron may compute a Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as using the spread values. The resulting value may then be passed to the summation layer. In the summation layer, the value coming out of a neuron in the hidden layer may be multiplied by a weight associated with the neuron and may add to the weighted values of other neurons. This sum becomes the output. For classification problems, one output is produced (with a separate set of weights and summation units) for each target category. The value output for a category is the probability that the case being evaluated has that category. In training of an RBF, various parameters may be determined, such as the number of neurons in a hidden layer, the coordinates of the center of each hidden-layer function, the spread of each function in each dimension, and the weights applied to outputs as they pass to the summation layer. Training may be used by clustering algorithms (such as k-means clustering), by evolutionary approaches, and the like.
In embodiments, a recurrent neural network may have a time-varying, real-valued (more than just zero or one) activation (output). Each connection may have a modifiable real-valued weight. Some of the nodes are called labeled nodes, some output nodes, and others hidden nodes. For supervised learning in discrete time settings, training sequences of real-valued input vectors may become sequences of activations of the input nodes, one input vector at a time. At each time step, each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units from which it receives connections. The system can explicitly activate (independent of incoming signals) some output units at certain time steps.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing neural network, such as a Kohonen self-organizing neural network, such as for visualization of views of data, such as low-dimensional views of high-dimensional data. The self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with an industrial machine. In embodiments, the self-organizing neural network may be used to identify structures in data, such as unlabeled data, such as in data sensed from a range of vibration, acoustic, or other analog sensors in an industrial environment, where sources of the data are unknown (such as where vibrations may be coming from any of a range of unknown sources). The self-organizing neural network may organize structures or patterns in the data, such that they can be recognized, analyzed, and labeled, such as identifying structures as corresponding to vibrations induced by the movement of a floor, or acoustic signals created by high frequency rotation of a shaft of a somewhat distant machine.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a recurrent neural network, which may allow for a bi-directional flow of data, such as where connected units (e.g., neurons or nodes) form a directed cycle. Such a network may be used to model or exhibit dynamic temporal behavior, such as involved in dynamic systems, such as a wide variety of the industrial machines and devices described throughout this disclosure, such as a power generation machine operating at variable speeds or frequencies in variable conditions with variable inputs, a robotic manufacturing system, a refining system, or the like, where dynamic system behavior involves complex interactions that an operator may desire to understand, predict, control and/or optimize. For example, the recurrent neural network may be used to anticipate the state (such as a maintenance state, a fault state, an operational state, or the like), of an industrial machine, such as one performing a dynamic process or action. In embodiments, the recurrent neural network may use internal memory to process a sequence of inputs, such as from other nodes and/or from sensors and other data inputs from the industrial environment, of the various types described herein. In embodiments, the recurrent neural network may also be used for pattern recognition, such as for recognizing an industrial machine based on a sound signature, a heat signature, a set of feature vectors in an image, a chemical signature, or the like. In a non-limiting example, a recurrent neural network may recognize a shift in an operational mode of a turbine, a generator, a motor, a compressor, or the like, such as a gear shift, by learning to classify the shift from a training data set consisting of a stream of data from tri-axial vibration sensors and/or acoustic sensors applied to one or more of such machines.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a modular neural network, which may comprise a series of independent neural networks (such as ones of various types described herein) that are moderated by an intermediary. Each of the independent neural networks in the modular neural network may work with separate inputs, accomplishing subtasks that make up the task the modular network as whole is intended to perform. For example, a modular neural network may comprise a recurrent neural network for pattern recognition, such as to recognize what type of industrial machine is being sensed by one or more sensors that are provided as input channels to the modular network and an RBF neural network for optimizing the behavior of the machine once understood. The intermediary may accept inputs of each of the individual neural networks, process them, and create output for the modular neural network, such an appropriate control parameter, a prediction of state, or the like.
Combinations among any of the pairs, triplets, or larger combinations, of the various neural network types described herein, are encompassed by the present disclosure. This may include combinations where an expert system uses one neural network for recognizing a pattern (e.g., a pattern indicating a problem or fault condition) and a different neural network for self-organizing an activity or work flow based on the recognized pattern (such as providing an output governing autonomous control of a system in response to the recognized condition or pattern). This may also include combinations where an expert system uses one neural network for classifying an item (e.g., identifying a machine, a component, or an operational mode) and a different neural network for predicting a state of the item (e.g., a fault state, an operational state, an anticipated state, a maintenance state, or the like). Modular neural networks may also include situations where an expert system uses one neural network for determining a state or context (such as a state of a machine, a process, a work flow, a marketplace, a storage system, a network, a data collector, or the like) and a different neural network for self-organizing a process involving the state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, or other process described herein).
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a physical neural network where one or more hardware elements is used to perform or simulate neural behavior. In embodiments, one or more hardware neurons may be configured to stream voltage values that represent analog vibration sensor data voltage values, to calculate velocity information from analog sensor inputs representing acoustic, vibration or other data, to calculation acceleration information from sensor inputs representing acoustic, vibration, or other data, or the like. One or more Hardware nodes may be configured to stream output data resulting from the activity of the neural net. Hardware nodes, which may comprise one or more chips, microprocessors, integrated circuits, programmable logic controllers, application-specific integrated circuits, field-programmable gate arrays, or the like, may be provided to optimize the speed, input/output efficiency, energy efficiency, signal to noise ratio, or other parameter of some part of a neural net of any of the types described herein. Hardware nodes may include hardware for acceleration of calculations (such as dedicated processors for performing basic or more sophisticated calculations on input data to provide outputs, dedicated processors for filtering or compressing data, dedicated processors for de-compressing data, dedicated processors for compression of specific file or data types (e.g., for handling image data, video streams, acoustic signals, vibration data, thermal images, heat maps, or the like), and the like. A physical neural network may be embodied in a data collector, such as a mobile data collector described herein, including one that may be reconfigured by switching or routing inputs in varying configurations, such as to provide different neural net configurations within the data collector for handling different types of inputs (with the switching and configuration optionally under control of an expert system, which may include a software-based neural net located on the data collector or remotely). A physical, or at least partially physical, neural network may include physical hardware nodes located in a storage system, such as for storing data within an industrial machine or in an industrial environment, such as for accelerating input/output functions to one or more storage elements that supply data to or take data from the neural net. A physical, or at least partially physical, neural network may include physical hardware nodes located in a network, such as for transmitting data within, to or from an industrial environment, such as for accelerating input/output functions to one or more network nodes in the net, accelerating relay functions, or the like. In embodiments of a physical neural network, an electrically adjustable resistance material may be used for emulating the function of a neural synapse. In embodiments, the physical hardware emulates the neurons, and software emulates the neural network between the neurons. In embodiments, neural networks complement conventional algorithmic computers. They are versatile and can be trained to perform appropriate functions without the need for any instructions, such as classification functions, optimization functions, pattern recognition functions, control functions, selection functions, evolution functions, and others.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a multilayered feed forward neural network, such as for complex pattern classification of one or more items, phenomena, modes, states, or the like. In embodiments, a multilayered feed forward neural network may be trained by an optimization technical, such as a genetic algorithm, such as to explore a large and complex space of options to find an optimum, or near-optimum, global solution. For example, one or more genetic algorithms may be used to train a multilayered feed forward neural network to classify complex phenomena, such as to recognize complex operational modes of industrial machines, such as modes involving complex interactions among machines (including interference effects, resonance effects, and the like), modes involving non-linear phenomena, such as impacts of variable speed shafts, which may make analysis of vibration and other signals difficult, modes involving critical faults, such as where multiple, simultaneous faults occur, making root cause analysis difficult, and others. In embodiments, a multilayered feed forward neural network may be used to classify results from ultrasonic monitoring or acoustic monitoring of an industrial machine, such as monitoring an interior set of components within a housing, such as motor components, pumps, valves, fluid handling components, and many others, such as in refrigeration systems, refining systems, reactor systems, catalytic systems, and others.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various industrial environments. In embodiments, the MLP neural network may be used for classification of physical environments, such as mining environments, exploration environments, drilling environments, and the like, including classification of geological structures (including underground features and above ground features), classification of materials (including fluids, minerals, metals, and the like), and other problems. This may include fuzzy classification.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a structure-adaptive neural network, where the structure of a neural network is adapted, such as based on a rule, a sensed condition, a contextual parameter, or the like. For example, if a neural network does not converge on a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some amount of training, the neural network may be modified, such as from a feed forward neural network to a recurrent neural network, such as by switching data paths between some subset of nodes from unidirectional to bi-directional data paths. The structure adaptation may occur under control of an expert system, such as to trigger adaptation upon occurrence of a trigger, rule or event, such as recognizing occurrence of a threshold (such as an absence of a convergence to a solution within a given amount of time) or recognizing a phenomenon as requiring different or additional structure (such as recognizing that a system is varying dynamically or in a non-linear fashion). In one non-limiting example, an expert system may switch from a simple neural network structure like a feed forward neural network to a more complex neural network structure like a recurrent neural network, a convolutional neural network, or the like upon receiving an indication that a continuously variable transmission is being used to drive a generator, turbine, or the like in a system being analyzed.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (MLP) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them. However, the output layer in the auto-encoder may have the same number of units as the input layer, where the purpose of the MLP neural network is to reconstruct its own inputs (rather than just emitting a target value). Therefore, the auto encoders may operate as an unsupervised learning model. An auto encoder may be used, for example, for unsupervised learning of efficient codings, such as for dimensionality reduction, for learning generative models of data, and the like. In embodiments, an auto-encoding neural network may be used to self-learn an efficient network coding for transmission of analog sensor data from an industrial machine over one or more networks. In embodiments, an auto-encoding neural network may be used to self-learn an efficient storage approach for storage of streams of analog sensor data from an industrial environment.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a probabilistic neural network (PNN), which in embodiments may comprise a multi-layer (e.g., four-layer) feed forward neural network, where layers may include input layers, hidden layers, pattern/summation layers and an output layer. In an embodiment of a PNN algorithm, a parent probability distribution function (PDF) of each class may be approximated, such as by a Parzen window and/or a non-parametric function. Then, using the PDF of each class, the class probability of a new input is estimated, and Bayes' rule may be employed, such as to allocate it to the class with the highest posterior probability. A PNN may embody a Bayesian network and may use a statistical algorithm or analytic technique, such as Kernel Fisher discriminant analysis technique. The PNN may be used for classification and pattern recognition in any of a wide range of embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict a fault condition of an engine based on collection of data inputs from sensors and instruments for the engine.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a time delay neural network (TDNN), which may comprise a feed forward architecture for sequential data that recognizes features independent of sequence position. In embodiments, to account for time shifts in data, delays are added to one or more inputs, or between one or more nodes, so that multiple data points (from distinct points in time) are analyzed together. A time delay neural network may form part of a larger pattern recognition system, such as using a perceptron network. In embodiments, a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback. In embodiments, a TDNN may be used to process sensor data from distinct streams, such as a stream of velocity data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, and the like, where time delays are used to align the data streams in time, such as to help understand patterns that involve understanding of the various streams (e.g., where increases in pressure and acceleration occur as an industrial machine overheats).
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a convolutional neural network (referred to in some cases as a CNN, a ConvNet, a shift invariant neural network, or a space invariant neural network), wherein the units are connected in a pattern similar to the visual cortex of the human brain. Neurons may respond to stimuli in a restricted region of space, referred to as a receptive field. Receptive fields may partially overlap, such that they collectively cover the entire (e.g., visual) field. Node responses can be calculated mathematically, such as by a convolution operation, such as using multilayer perceptrons that use minimal preprocessing. A convolutional neural network may be used for recognition within images and video streams, such as for recognizing a type of machine in a large environment using a camera system disposed on a mobile data collector, such as on a drone or mobile robot. In embodiments, a convolutional neural network may be used to provide a recommendation based on data inputs, including sensor inputs and other contextual information, such as recommending a route for a mobile data collector. In embodiments, a convolutional neural network may be used for processing inputs, such as for natural language processing of instructions provided by one or more parties involved in a workflow in an environment. In embodiments, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), with multiple (e.g., 4, 5, 6 or more) layers, and with many (e.g., millions) parameters. A convolutional neural net may use one or more convolutional nets.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a regulatory feedback network, such as for recognizing emergent phenomena (such as new types of faults not previously understood in an industrial environment).
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a self-organizing map (SOM), involving unsupervised learning. A set of neurons may learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM may preserve these while mapping phenomena into groups.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a learning vector quantization neural net (LVQ). Prototypical representatives of the classes may parameterize, together with an appropriate distance measure, in a distance-based classification scheme.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an echo state network (ESN), which may comprise a recurrent neural network with a sparsely connected, random hidden layer. The weights of output neurons may be changed (e.g., the weights may be trained based on feedback). In embodiments, an ESN may be used to handle time series patterns, such as, in an example, recognizing a pattern of events associated with a gear shift in an industrial turbine, generator, or the like.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a bi-directional, recurrent neural network (BRNN), such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the past and the future context of the element. This may be done by adding the outputs of two RNNs, such as one processing the sequence from left to right, the other one from right to left. The combined outputs are the predictions of target signals, such as ones provided by a teacher or supervisor. A bi-directional RNN may be combined with a long short-term memory RNN.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical RNN that connects elements in various ways to decompose hierarchical behavior, such as into useful subprograms. In embodiments, a hierarchical RNN may be used to manage one or more hierarchical templates for data collection in an industrial environment.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a stochastic neural network, which may introduce random variations into the network. Such random variations can be viewed as a form of statistical sampling, such as Monte Carlo sampling.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a genetic scale recurrent neural network. In such embodiments, a RNN (often a LSTM) is used where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node. The outputs from all the various scales may be treated as a committee of members, and the associated scores may be used genetically for the next iteration.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a committee of machines (CoM), comprising a collection of different neural networks that together “vote” on a given example. Because neural networks may suffer from local minima, starting with the same architecture and training, but using randomly different initial weights often gives different results. A CoM tends to stabilize the result.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an associative neural network (ASNN), such as involving an extension of committee of machines that combines multiple feed forward neural networks and a k-nearest neighbor technique. It may use the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the bias of the neural network ensemble. An associative neural network may have a memory that can coincide with a training set. If new data become available, the network instantly improves its predictive ability and provides data approximation (self-learns) without retraining. Another important feature of ASNN is the possibility to interpret neural network results by analysis of correlations between data cases in the space of models.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an instantaneously trained neural network (ITNN), where the weights of the hidden and the output layers are mapped directly from training vector data.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a spiking neural network, which may explicitly consider the timing of inputs. The network input and output may be represented as a series of spikes (such as a delta function or more complex shapes). SNNs can process information in the time domain (e.g., signals that vary over time, such as signals involving dynamic behavior of industrial machines). They are often implemented as recurrent networks.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a dynamic neural network that addresses nonlinear multivariate behavior and includes learning of time-dependent behavior, such as transient phenomena and delay effects. Transients may include behavior of shifting industrial components, such as variable speeds of rotating shafts or other rotating components.
In embodiments, cascade correlation may be used as an architecture and supervised learning algorithm, supplementing adjustment of the weights in a network of fixed topology. Cascade-correlation may begin with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights may be frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The cascade-correlation architecture may learn quickly, determine its own size and topology, and retain the structures it has built even if the training set changes and requires no back-propagation.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a neuro-fuzzy network, such as involving a fuzzy inference system in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in a fuzzy inference, such as fuzzification, inference, aggregation and defuzzification. Embedding a fuzzy system in a general structure of a neural net as the benefit of using available training methods to find the parameters of a fuzzy system.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a compositional pattern-producing network (CPPN), such as a variation of an associative neural network (ANN) that differs the set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs can include both types of functions and many others. Furthermore, CPPNs may be applied across the entire space of possible inputs, so that they can represent a complete image. Since they are compositions of functions, CPPNs in effect encode images at infinite resolution and can be sampled for a particular display at whatever resolution is optimal
This type of network can add new patterns without re-training. In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a one-shot associative memory network, such as by creating a specific memory structure, which assigns each new pattern to an orthogonal plane using adjacently connected hierarchical arrays.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a hierarchical temporal memory (HTM) neural network, such as involving the structural and algorithmic properties of the neocortex. HTM may use a biomimetic model based on memory-prediction theory. HTM may be used to discover and infer the high-level causes of observed input patterns and sequences.
In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a holographic associative memory (HAM) neural network, which may comprise an analog, correlation-based, associative, stimulus-response system. Information may be mapped onto the phase orientation of complex numbers. The memory is effective for associative memory tasks, generalization and pattern recognition with changeable attention.
In embodiments, various embodiments involving network coding may be used to code transmission data among network nodes in neural net, such as where nodes are located in one or more data collectors or machines in an industrial environment.
In embodiments, one or more of the controllers, circuits, systems, data collectors, storage systems, network elements, or the like as described throughout this disclosure may be embodied in or on an integrated circuit, such as an analog, digital, or mixed signal circuit, such as a microprocessor, a programmable logic controller, an application-specific integrated circuit, a field programmable gate array, or other circuit, such as embodied on one or more chips disposed on one or more circuit boards, such as to provide in hardware (with potentially accelerated speed, energy performance, input-output performance, or the like) one or more of the functions described herein. This may include setting up circuits with up to billions of logic gates, flip-flops, multiplexers, and other circuits in a small space, facilitating high speed processing, low power dissipation, and reduced manufacturing cost compared with board-level integration. In embodiments, a digital IC, typically a microprocessor, digital signal processor, microcontroller, or the like may use Boolean algebra to process digital signals to embody complex logic, such as involved in the circuits, controllers, and other systems described herein. In embodiments, a data collector, an expert system, a storage system, or the like may be embodied as a digital integrated circuit, such as a logic IC, memory chip, interface IC (e.g., a level shifter, a serializer, a deserializer, and the like), a power management IC and/or a programmable device; an analog integrated circuit, such as a linear IC, RF IC, or the like, or a mixed signal IC, such as a data acquisition IC (including A/D converters, D/A converter, digital potentiometers) and/or a clock/timing IC.
1. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising:
A modular neural network, where the expert system uses one type of neural network for recognizing a pattern and a different neural network for self-organizing an activity in the industrial environment.
2. A system of clause 1, wherein the pattern indicates a fault condition of a machine.
3. A system of clause 1, wherein the self-organized activity governs autonomous control of a system in the environment.
4. A system of clause 3, wherein the expert system organizes the activity based at least in part on the recognized pattern.
5. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising:
a modular neural network, where the expert system uses one neural network for classifying an item and a different neural network for predicting a state of the item.
6. A system of clause 5, wherein classifying an item includes at least one of identifying a machine, a component, and an operational mode of a machine in the environment.
7. A system of clause 5, wherein predicting a state includes predicting at least one of a fault state, an operational state, an anticipated state, and a maintenance state.
8. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising:
a modular neural network, where the expert system uses one neural network for determining at least one of a state and a context and a different neural network for self-organizing a process involving the at least one state or context.
9. A system of clause 8, wherein the stat or context includes at least one state of a machine, a process, a work flow, a marketplace, a storage system, a network, and a data collector.
10. A system of clause 8, wherein the self-organized process includes at least one of a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, and a boring process.
11. An expert system for processing a plurality of inputs collected from sensors in an industrial environment, comprising:
a modular neural network, comprising at least two neural networks selected from the group consisting of feed forward neural networks, radial basis function neural networks, self-organizing neural networks, Kohonen self-organizing neural networks, recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layered neural networks, convolutional neural networks, a hybrids of a neural networks with another expert system, auto-encoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning vector quantization (LVQ) neural networks, fully recurrent neural networks, simple recurrent neural networks, echo state neural networks, long short-term memory neural networks, bi-directional neural networks, hierarchical neural networks, stochastic neural networks, genetic scale RNN neural networks, committee of machines neural networks, associative neural networks, physical neural networks, instantaneously trained neural networks, spiking neural networks, neocognitron neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, compositional pattern-producing neural networks, memory neural networks, hierarchical temporal memory neural networks, deep feed forward neural networks, gated recurrent unit (GCU) neural networks, auto encoder neural networks, variational auto encoder neural networks, de-noising auto encoder neural networks, sparse auto-encoder neural networks, Markov chain neural networks, restricted Boltzmann machine neural networks, deep belief neural networks, deep convolutional neural networks, de-convolutional neural networks, deep convolutional inverse graphics neural networks, generative adversarial neural networks, liquid state machine neural networks, extreme learning machine neural networks, echo state neural networks, deep residual neural networks, support vector machine neural networks, neural Turing machine neural networks, and holographic associative memory neural networks.
12. A system for collecting data in an industrial environment, comprising A physical neural network embodied in a mobile data collector, wherein the mobile data collector is adapted to be reconfigured by routing inputs in varying configurations, such that different neural net configurations are enabled within the data collector for handling different types of inputs
13. A system of clause 12, wherein reconfiguration occurs under control of an expert system.
14. A system of clause 13, wherein the expert system includes a software-based neural net.
15. A system of clause 14, wherein the software-based system is located on the data collector.
16. A system of clause 14, wherein the software-based system is located remotely from the data collector.
17. A system for processing data collected from an industrial environment, the system comprising:
a plurality of neural networks deployed in a cloud platform that receives data streams and other inputs collected from one or more industrial environments and transmitted to the cloud platform over one or more networks, wherein the neural networks are of different types.
18. A system of clause 17, wherein the plurality of neural networks includes at least one modular neural network.
19. A system of clause 17, wherein the plurality of neural networks includes at least one structure-adaptive neural network.
20. A system of clause 17, wherein the neural networks are structured to compete with each other under control of an expert system, such as by processing input data sets from the same industrial environment to provide outputs and comparing the outputs to at least one measure of success.
21. A system of clause 20, wherein a genetic algorithm is used to facilitate variation and selection for the competing neural networks.
22. A system of clause 20, wherein the measure of success includes at least one of a measure of predictive accuracy, a measure of classification accuracy, an efficiency measure, a profit measure, a maintenance measure, a safety measure, and a yield measure.
23. A system, comprising:
a network coding system for coding transmission of data among network nodes in neural network, wherein the nodes comprise hardware devices located in at least one of one or more data collectors, one or more storage systems, and one or more network devices located in an industrial environment.
Within the data collection, monitoring, and control environment of the industrial Internet of Things are large and various sensor sets, which make efficient setup and timely changes to sensor data collection a challenge. Continuous collection from all sensors may be impossible given the large number of sensors and limited resources, such as limited availability of power and limited data collection and management facilities, including various limitations in availability and performance of sensor data collection devices, input/output interfaces, data transfer facilities, data storage, data analysis facilities, and the like. The number of sensors collected from at any given time must therefore be limited in an intelligent but timely manner, both at the time of setting up initial collection and during the process of collection, including handling rapid changes to a present collection scheme based on a change in state of a system, operational conditions (e.g., an alert condition, change in operational mode, and the like) or the like. Embodiments of the methods and systems disclosed herein may therefore include rapid route creation and modification for routing collectors, such as by taking advantage of hierarchical templates, execution of smart route changes, monitoring and responding to changes in operational conditions, and the like.
In embodiments, rapid route creation and modification for data collection in an industrial environment may take advantage of hierarchical templates. Templates may be used to take advantage of ‘like’ machinery that can utilize the same hierarchical sensor routing scheme. For example, among many possible types of machines about which data may be collected, the members of a certain class of motor, such as a stepper motor class, may have very similar sensor routing needs, such as for routine operations, routine maintenance, and failure mode detection, that may be described in a common hierarchy of sensor collection routines. The user installing a new stepper motor may then use the ‘stepper motor hierarchical routing template’ for the new motor. After installation, the stepper motor hierarchical routing template may then be used to change the routing schemes for changing conditions. The user may optionally make adjustments to the template as needed per unique motor functions, applications, environments, modes, and the like. The use of a template for deploying a routing scheme greatly reduces the time a user requires to configure the routing scheme for a new motor, or to deploy new routing technologies on an existing system that utilizes traditional sensor collection methods. Once the hierarchical routing template is in place, the sensor collection routine may be changed quickly based on the template, thus, allowing for rapid route modification under changing conditions, such as a change in the operating mode of the stepper motor that requires a different subset of sensors for monitoring, a limit alert or failure indication that requires a more focused subset of sensors for use in diagnosing the problem, and the like. Hierarchical routing templates thus allow for rapid deployment of sensor routing configurations, as well as allowing the sensed industrial environment to be altered dynamically as conditions change.
A functional hierarchy of routing templates may include different hierarchical configurations for a component, machine, system, industrial environment, and the like, including all sensors and a plurality of configurations formed from a subset of all sensors. At a system level, an ‘all-sensor’ configuration may include a connection map to all sensors in a system, mapping to all onboard instrumentation sensors (e.g., monitoring points reporting within a machine or set of machines), mapping to an environment's sensors (e.g., monitoring points around the machines/equipment, but not necessarily onboard), mapping to available sensors on data collectors (e.g., data collectors that can be flexibly provisioned for particular data among different kinds), a unified map combining different individual mappings, and the like. A routing configuration may be provided, such as indicate how to implement an operational routing scheme, a scheduled maintenance routing scheme (e.g., collecting from a greater set of overall sensors than in operational mode, but distributed across the system, or a focused sensor set for specific components, functions, and modes), one or more failure mode routing schemes for multiple focused sensor collection groups targeting different failure mode analyses (e.g., for a motor, one failure mode may be for bearings, another for startup speed-torque, where a different subset of sensor data is needed based on the failure mode, such as detected in anomalous readings taken during operations or maintenance), power savings (e.g., weather conditions necessitating reduced plant power), and the like.
As noted, hierarchical templates may also be conditional (e.g., rule-based), such as templates with conditional routing based on parameters, such as sensed data during a first collection period, where a subsequent routing configuration is varied. Within the hierarchy, nodes in a graph or tree may indicate forks by which conditional logic may be used, such as to select a given subset of sensors for a given operational mode. Thus, the hierarchical template may be associated with a rule-based or model-based expert system, which may facilitate automated routing based on the hierarchical template and based on observed conditions, such as based on a type of machine and its operational state, environmental context, or the like. In a non-limiting example, a hierarchical template may have an initial collection configuration and a conditional hierarchy in place to switch from the initial collection configuration to a second collection configuration based on the sensed conditions of an initial sensor collection. Continuing this example, among various possible machines, a conveyor system may have a plurality of sensors for collection in an initial collection, but once the first data is collected and analyzed, if the conveyor is determined to be in an idle state (such as due to the absence of a signal above a minimum threshold on a motion sensor), then the system may switch to a sensor data collection regime that is appropriate for the idles state of the conveyor (e.g., using a very small subset of the plurality of sensors, such as just using the motion sensor to detect departure from the idle state, at which point the original regime may be renewed and the rest of a sensor set may be re-engaged). Thus, when the collection of sensor data detects a changed condition to a state, an operational mode, an environmental condition, or the like, the sensor data collection may be switched to an appropriate configuration.
Hierarchical templates for one collector may be based on coordination of routing with that of other collectors. For instance, a collector might be set up to perform vibration analysis while another collector is set up to perform pressure or temperature on each machine in a set of similar machines, rather than having each machine collect all of the data on each machine, where otherwise setup for different sensor types may be required for each collector for each machine. Factors such as the duration of sampling required, the time required to set up a given sensor, the amount of power consumed, the time available for collection as a whole, the data rate of input/output of a sensor and/or the collector, the bandwidth of a channel (wired or wireless) available for transmission of collected data, and the like can be considered in arranging the coordination of the routing of two or more collectors, such that various parallel and serial configurations may be undertaken to achieve an overall effectiveness. This may include optimizing the coordination using an expert system, such as a rule-based optimization, a model-based optimization, or optimization using machine learning.
A machine learning system may create a hierarchical template structure for improved routing, such as for teaching the system the default operating conditions (e.g., normal operations mode, systems online and average production), peak operations mode (max capability), slack production, and the like. The machine learning system may create a new hierarchical template based on monitored conditions, such as based on a production level profile, a rate of production profile, a detected failure mode pattern analysis, and the like. The application of a new machine learning created template may be based on a mode matching between current production conditions and a machine learning template condition (e.g., the machine learning system creates a new template for a new production profile, and applies that new template whenever that new profile is detected).
Rapid route creation may be enabled using one or more hierarchical routing templates, such as when a routing template pre-establishes a routing scheme for different conditions, and where a trigger event executes a change in the sensor routing scheme to accommodate the condition. In embodiments, the trigger event may be an automatic change in routing based on a trigger that indicates a possible failure mode that forces a change in routing scheme from operational to failure mode analysis, a human-executed change in routing scheme based on received sensor data, a learned routing change based on machine learning of when to trigger a change (e.g., as based on a machine being fed with a set of human-executed or human-supervised changes), a manual routing change (e.g., optional to automatic/rapid automatic change), a human executed change based on observed device performance, and the like. Routing changes may include for instance, changing from an operational mode to an accelerated maintenance, a failure mode analysis, a power-savings, a high-performance/high-output mode (e.g., for peak power in a generation plant), and the like.
Switching hierarchical template configurations may be executed based on connectivity with end-device sensors. In a highly automated collection routing environment (e.g., an indoor networked assembly plant) different routing collection configurations may be employed for fixed and flexible industrial layouts. In a fixed industrial layout, such as with a high degree of wired connectivity between end-device sensors, automated collectors, and networks, there may be different routing configurations for a network routing hierarchy portion, a collector sensor-collection hierarchy portion, a storage portion, and the like. For a more flexible industrial layout with various wired and wireless connections between end-device sensors, automated collectors, and networks, there may be different schemes. For instance, a moderately automated collection routing environment may include automatic collection and periodic network connection, a robot-carried collector for periodic collection (e.g., a ground-based robot, a drone, an underwater device, a robot with network connection, a robot with intermittent network connection, a robot that periodically uploads collection), a routing scheme with periodic collection and automated routing, a scheme only collecting periodically but route directly upon collection, a routing scheme with periodic collection and periodic automated routing to collect periodically, and, over longer periods of time, periodically route multiple collections, and the like. For a lower degree of automated collection routing there may be a combination of automatic collection and human-aided collectors (e.g., humans collecting alone, humans aided by robots), scheduled collection and human-aided collectors (e.g., humans initiating collection, humans aided by robots for collection initiation, human launching a drone to collect data at a remote site), and the like.
In embodiments, and referring to
In embodiments, evaluation of the current routing templates may be based on operational mode routing collection schemes, such as a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, a power savings operational mode, and the like. As a result of monitoring the data collector may switch from a current routing template collection routine because the data analysis circuit determines a change in operating modes, such as the operating mode changing from an operational mode to an accelerated maintenance mode, the operating mode changing from an operational mode to a failure mode analysis mode, the operating mode changing from an operational mode to a power-savings mode, the operating mode changing from an operational mode to a high-performance mode, and the like. The data collector may switch from a current routing template collection routine based on a sensed change in a mode of operation, such as a failure condition, a performance condition, a power condition, a temperature condition, a vibration condition, and the like. The evaluation of the current routing template collection routine may be based on a collection routine with respect to a collection parameter, such as network availability, sensor availability, a time-based collection routine (e.g., on a schedule, over time), and the like.
1. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data storage structured to store a plurality of collector route templates and sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
a data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data, wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data.
2. The system of clause 1, wherein the system is deployed locally on the data collector.
3. The system of clause 1, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
4. The system of clause 1, wherein each of the input channels corresponds to a sensor located in the environment.
5. The system of clause 1, wherein the evaluation of the current routing template is based on operational mode routing collection schemes.
6. The system of clause 5, wherein the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power savings operational mode.
7. The system of clause 1, wherein the data collector switches from the current routing template collection routine because the data analysis circuit determines a change in operating modes.
8. The system of clause 7, wherein the operating mode changed from an operational mode to an accelerated maintenance mode.
9. The system of clause 7, wherein the operating mode changed from an operational mode to a failure mode analysis mode.
10. The system of clause 7, wherein the operating mode changed from an operational mode to a power-savings mode.
11. The system of clause 7, wherein the operating mode changed from an operational mode to high-performance mode.
12. The system of clause 1, wherein the data collector switches from the current routing template collection routine based on a sensed change in a mode of operation.
13. The system of clause 12, wherein the sensed change is a failure condition.
14. The system of clause 12, wherein the sensed change is a performance condition.
15. The system of clause 12, wherein the sensed change is a power condition.
16. The system of clause 12, wherein the sensed change is a temperature condition.
17. The system of clause 12, wherein the sensed change is a vibration condition.
18. The system of clause 1, wherein the evaluation of the current routing template collection routine is based on a collection routine with respect to a collection parameter.
19. The system of clause 18, wherein the parameter is network availability.
20. The system of clause 18, wherein the parameter is sensor availability.
21. The system of clause 18, wherein the parameter is a time-based collection routine.
22. The system of clause 21, wherein the time-based collection routine collects sensor data on a schedule.
23. The system of clause 21, wherein the time-based collection routing evaluates sensor data over time.
24. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a plurality of collector route templates and sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data,
wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data.
25. The method of clause 25, wherein the computer-implemented method is deployed locally on the data collector.
26. The method of clause 25, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
27. The method of clause 25, wherein each of the input channels corresponds to a sensor located in the environment.
28. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a plurality of collector route templates and sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data,
wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the content of the output data.
29. The one or more non-transitory computer-readable media of clause 29, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
30. The one or more non-transitory computer-readable media of clause 29, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
31. The one or more non-transitory computer-readable media of clause 29, wherein each of the input channels corresponds to a sensor located in the environment.
32. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
a machine learning data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data received over time, wherein the machine learning data analysis circuit learns received output data patterns,
wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the learned received output data patterns.
33. The system of clause 32, wherein the system is deployed locally on the data collector.
34. The system of clause 32, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
35. The system of clause 32, wherein each of the input channels corresponds to a sensor located in the environment.
36. The system of clause 32, wherein the machine learning data analysis circuit comprises a neural network expert system.
37. The system of clause 32, wherein the evaluation of the current routing template is based on operational mode routing collection schemes.
38. The system of clause 37, wherein the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power savings operational mode.
39. The system of clause 32, wherein the data collector switches from the current routing template collection routine because the data analysis circuit determines a change in operating modes.
40. The system of clause 39, wherein the operating mode changed from an operational mode to an accelerated maintenance mode.
41. The system of clause 39, wherein the operating mode changed from an operational mode to a failure mode analysis mode.
42. The system of clause 39, wherein the operating mode changed from an operational mode to a power-savings mode.
43. The system of clause 39, wherein the operating mode changed from an operational mode to high-performance mode.
44. The system of clause 32, wherein the data collector switches from the current routing template collection routine based on a sensed change in a mode of operation.
45. The system of clause 44, wherein the sensed change is a failure condition.
46. The system of clause 44, wherein the sensed change is a performance condition.
47. The system of clause 44, wherein the sensed change is a power condition.
48. The system of clause 44, wherein the sensed change is a temperature condition.
49. The system of clause 44, wherein the sensed change is a vibration condition.
50. The system of clause 32, wherein the evaluation of the current routing template collection routine is based on a collection routine with respect to a collection parameter.
51. The system of clause 50, wherein the parameter is network availability.
52. The system of clause 50, wherein the parameter is sensor availability.
53. The system of clause 50, wherein the parameter is a time-based collection routine.
54. The system of clause 53, wherein the time-based collection routine collects sensor data on a schedule.
55. The system of clause 53, wherein the time-based collection routing evaluates sensor data over time.
56. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
providing a machine learning data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data received over time,
wherein the machine learning data analysis circuit learns received output data patterns,
wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the learned received output data patterns.
57. The method of clause 56, wherein the computer-implemented method is deployed locally on the data collector.
58. The method of clause 56, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
59. The method of clause 56, wherein each of the input channels corresponds to a sensor located in the environment.
60. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
providing a machine learning data analysis circuit structured to receive output data from the plurality of input channels and evaluate a current routing template collection routine based on the received output data received over time,
wherein the machine learning data analysis circuit learns received output data patterns,
wherein the data collector is configured to switch from the current routing template collection routine to an alternative routing template collection routine based on the learned received output data patterns.
61. The one or more non-transitory computer-readable media of clause 60, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
62. The one or more non-transitory computer-readable media of clause 60, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
63. The one or more non-transitory computer-readable media of clause 60, wherein each of the input channels corresponds to a sensor located in the environment.
64. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data storage structured to store a collector route template, sensor specifications for sensors that correspond to the input channels, wherein the collector route template comprises a sensor collection routine;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
a data analysis circuit structured to receive output data from the plurality of input channels and evaluate the received output data with respect to a rule,
wherein the data collector is configured to modify the sensor collection routine based on the application of the rule to the received output data.
65. The system of clause 64, wherein the system is deployed locally on the data collector.
66. The system of clause 64, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
67. The system of clause 64, wherein each of the input channels corresponds to a sensor located in the environment.
68. The system of clause 64, wherein the rule is based on an operational state of a machine with respect to which the input channels provide information.
69. The system of clause 64, wherein the rule is based on an anticipated state of a machine with respect to which the input channels provide information.
70. The system of clause 64, wherein the rule is based on a detected fault condition of a machine with respect to which the input channels provide information.
71. The system of clause 64, wherein the evaluation of the received output data is based on operational mode routing collection schemes.
72. The system of clause 71, wherein the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power savings operational mode.
73. The system of clause 64, wherein the data collector modifies the sensor collection routine because the data analysis circuit determines a change in operating modes.
74. The system of clause 73, wherein the operating mode changed from an operational mode to an accelerated maintenance mode.
75. The system of clause 73, wherein the operating mode changed from an operational mode to a failure mode analysis mode.
76. The system of clause 73, wherein the operating mode changed from an operational mode to a power-savings mode.
77. The system of clause 73, wherein the operating mode changed from an operational mode to high-performance mode.
78. The system of clause 64, wherein the data collector modifies the sensor collection routine based on a sensed change in a mode of operation.
79. The system of clause 78, wherein the sensed change is a failure condition.
80. The system of clause 78, wherein the sensed change is a performance condition.
81. The system of clause 78, wherein the sensed change is a power condition.
82. The system of clause 78, wherein the sensed change is a temperature condition.
83. The system of clause 78, wherein the sensed change is a vibration condition.
84. The system of clause 64, wherein the evaluation of the received output data is based on a collection routine with respect to a collection parameter.
85. The system of clause 84, wherein the parameter is network availability.
86. The system of clause 84, wherein the parameter is sensor availability.
87. The system of clause 84, wherein the parameter is a time-based collection routine.
88. The system of clause 87, wherein the time-based collection routine collects sensor data on a schedule.
89. The system of clause 87, wherein the time-based collection routing evaluates sensor data over time.
90. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a collector route template, sensor specifications for sensors that correspond
to the input channels, wherein the collector route template comprises a sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate the received output data with respect to a rule,
wherein the data collector is configured to modify the sensor collection routine based on the application of the rule to the received output data.
91. The method of clause 90, wherein the computer-implemented method is deployed locally on the data collector.
92. The method of clause 90, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
93. The method of clause 90, wherein each of the input channels corresponds to a sensor located in the environment.
94. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a collector route template, sensor specifications for sensors that correspond
to the input channels, wherein the collector route template comprises a sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels; and
providing a data analysis circuit structured to receive output data from the plurality of input channels and evaluate the received output data with respect to a rule,
wherein the data collector is configured to modify the sensor collection routine based on the application of the rule to the received output data.
95. The one or more non-transitory computer-readable media of clause 94, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
96. The one or more non-transitory computer-readable media of clause 94, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
97. The one or more non-transitory computer-readable media of clause 94, wherein each of the input channels corresponds to a sensor located in the environment.
Rapid route creation and modification in an industrial environment may employ smart route changes based on incoming data or alarms, such as to enable dynamic selection of data collection for analysis or correlation. Smart route changes may enable the system to alter current routing of sensor data based on incoming data or alarms. For instance, a user may set up a routing configuration that establishes a schedule of sensor collection for analysis, but when the analysis (or an alarm) indicates a special need, the system may change the sensor routing to address that need. For example, in the case where a change in a motor vibration profile (as one example among any of the machines described throughout this disclosure), such as rapidly increasing the peak amplitude of shaking on at least one axis of a vibration sensor set, that indicates a potential early failure of the motor, the system may change the routing to collect more focused data collection for analysis, such as initiating collection on more axes of the motor, initiating collection on additional bearings of the motor, and/or initiating collection using other sensors (such as temperature or heat flux sensors), that may confirm an initial hypothesis that the failure mode is occurring or otherwise assist in analysis of the state or operational condition of the machine.
Detected operational mode changes may trigger a rapid route change. For instance, an operational mode may be detected as the result of a single-point sensor out-of-range detection, an analysis determination, and the like, and generate a routing change. An analysis determination may be detected from a sensor end-point, such as through a single-point sensor analysis, a multiple-point sensor analysis, an analysis domain analysis (e.g., through a time profile, frequency profile, correlated multi-point determination), and the like. In another instance, a maintenance mode may be detected during routine maintenance, where a routing change increases data collection to capture data at a higher rate under an anomalous condition. A failure mode may be detected, such as through an alarm that indicates near-term potential for a failure of a machine that triggers increased data capture rate for analysis. Performance-based modes may be detected, such as detecting a level of output rate (e.g., peak, slack, idle), which may then initiate changes in routing to accommodate the analysis needs for the different performance monitoring and metrics associated with the state. For example, if a high peak speed is detected for a motor, a conveyor, an assembly line, a generator, a turbine, or the like, relative to historical measurements over some time period, additional sensors may be engaged to watch for failures that are typically associated with peak speeds, such as overheating (as measured by engaging a temperature or heat flux sensor), excessive noise (as measured by an acoustic or noise sensor), excessive shaking (as measured by one or more vibration sensors), or the like.
Alarm detections may trigger a rapid route change. Alarm sources may include a front-end collector, local intelligence resource, back-end data analysis process, ambient environment detector, network quality detector, power quality detector, heat, smoke, noise, flooding, and the like. Alarm types may include a single-instance anomaly detection, multiple-instance anomaly detection, simultaneous multi-sensor detection, time-clustered sensor detection (e.g., a single sensor or multiple sensors), frequency-profile detection (e.g., increasing rate of anomaly detection such as an alarm increasing in its occurrence over time, a change in a frequency component of a sensor output such as a motor's physical vibration profile changing over time), and the like.
A machine learning system may change routing based on learned alarm pattern analysis. The machine learning system may learn system alarm condition patterns, such as alarm conditions expected under normal operating conditions, under peak operating conditions, expected over time based on age of components (e.g., new, during operational life, during extended life, during a warrantee period), and the like. The machine learning system may change routing based on a change in an alarm pattern, such as a system operating normally but experiencing a peak operating alarm pattern (e.g., a system running when it shouldn't be), a system is new but experiencing an older profile (e.g., detection of infant mortality), and the like. The machine learning system may change routing based on a current alarm profile vs. an expected change in production condition. For example, a plant, system, or component is experiencing above average alarm conditions just before a ramp-up of production (e.g., could be foretelling of above average failures during increased production), just before going slack (e.g., could be an opportunity to ramp up maintenance procedures based on increased data taking routing scheme), after an unplanned event (e.g., weather, power outage, restart), and the like.
A rapid route change action may include an increased rate of sampling (e.g., to a single sensor, to multiple sensors), increase in the number of sensors being sampled (e.g., simultaneous sampling of other sensors on a device, coordinated sampling of similar sensors on near-by devices), generating a burst of sampling (e.g., sampling at a high rate for a period of time), and the like. Actions may be executed on a schedule, coordinated with a trigger, based on an operational mode, and the like. Triggered actions may be from anomalous data, an exceeded threshold level, an operational event trigger (e.g., at startup condition such as for startup motor torque), and the like.
A rapid route change may switch between routing schemes, such as an operational routing scheme (e.g., a subset of sensor collection for normal operations), a scheduled maintenance routing scheme (e.g., an increased and focused set of sensor collection than for normal operations), and the like. The distribution of sensor data may be changed, such as to distribute sensor collection across the system, such as for a sensor collection set for specific components, functions, and modes. A failure mode routing scheme may entail multiple focused sensor collection groups targeting different failure mode analyses (e.g., for a motor, one failure mode may be for bearings, another for startup speed-torque) where a different subset of sensor data may be needed based on the failure mode (e.g., as detected in anomalous readings taken during operations or maintenance). Power savings mode routing may be executed when weather conditions necessitate reduced plant power.
Dynamic adjustment of route changes may be executed based on connectivity factors, such as associated with the collector or network availability and bandwidth. For example, routing may be changed for a device associated with an alarm detection, where changing routing for targeted devices on the network frees up bandwidth. Changes to routing may have a duration, such as only for a pre-determined period of time and then switching back, maintaining a change until user-directed, changing duration based on network availability, and the like.
In embodiments, and referring to
In embodiments, an alarm state may indicate a detection mode, such as an operational mode detection comprising an out-of-range detection, a maintenance mode detection comprising an alarm detected during maintenance, a failure mode detection (e.g., where the controller communicates a failure mode detection facility), a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information, a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information, and the like. The monitoring system may further include the analysis circuit setting the alarm state when the alarm threshold level is exceeded for an alternate input channel in the first group of input channels, such as where the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the second routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis. The second routing of input channels may include a change in a routing collection parameter, such as where the routing collection parameter is an increase in sampling rate, an increase in the number of channels being sampled, a burst sampling of at least one of the plurality of input channels, and the like.
In embodiments, and referring to
1. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
a data storage structured to store sensor specifications for sensors that correspond to the input channels;
a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; and
a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel.
2. The system of clause 1, wherein the system is deployed locally on the data collector.
3. The system of clause 1, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
4. The system of clause 1, wherein each of the input channels corresponds to a sensor located in the environment.
5. The system of clause 1, wherein the group of input channels is related to the first input channel are at least in part taken from the plurality of input channels not included in the first routing of input channels.
6. The system of clause 1, wherein the alarm state indicates a detection mode.
7. The system of clause 6, wherein the detection mode is an operational mode detection comprising an out-of-range detection.
8. The system of clause 6, wherein the detection mode is a maintenance mode detection comprising an alarm detected during maintenance.
9. The system of clause 6, wherein the detection mode is a failure mode detection.
10. The system of clause 9, wherein the controller communicates the failure mode detection facility.
11. The system of clause 6, wherein the detection mode is a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information.
12. The system of clause 6, wherein the detection mode is a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information.
13. The system of clause 1, further comprising the analysis circuit setting the alarm state when the alarm threshold level is exceeded for a alternate input channel in the first group of input channels.
14. The system of clause 13, wherein the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis.
15. The system of clause 1, wherein the alternate routing of input channels comprises a change in a routing collection parameter.
16. The system of clause 15, wherein the routing collection parameter is an increase in sampling rate.
17. The system of clause 15, wherein the routing collection parameter is an increase in the number of channels being sampled.
18. The system of clause 15, wherein the routing collection parameter comprises a burst sampling of at least one of the plurality of input channels.
19. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; and
providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel.
20. The method of clause 19, wherein the computer-implemented method is deployed locally on the data collector.
21. The method of clause 19, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
22. The method of clause 19, wherein each of the input channels corresponds to a sensor located in the environment.
23. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels; and
providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel.
24. The one or more non-transitory computer-readable media of clause 23, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
25. The one or more non-transitory computer-readable media of clause 23, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
26. The one or more non-transitory computer-readable media of clause 23, wherein each of the input channels corresponds to a sensor located in the environment.
27. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
a data storage structured to store sensor specifications for sensors that correspond to the input channels; a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels and transmits the alarm state across a network to a routing control facility; and
a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel, wherein the data collector automatically executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met.
28. The system of clause 27, wherein the system is deployed locally on the data collector.
29. The system of clause 27, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
30. The system of clause 27, wherein each of the input channels corresponds to a sensor located in the environment.
31. The system of clause 27, wherein the communication parameter is a time-period parameter within which the routing control facility must respond.
32. The system of clause 27, wherein the communication parameter is a network availability parameter.
33. The system of clause 32, wherein the network parameter is a network connection.
34. The system of clause 32, wherein the network parameter is a bandwidth requirement.
35. The system of clause 27, wherein the group of input channels is related to the first input channel are at least in part taken from the plurality of input channels not included in the first routing of input channels.
36. The system of clause 27, wherein the alarm state indicates a detection mode.
37. The system of clause 36, wherein the detection mode is an operational mode detection comprising an out-of-range detection.
38. The system of clause 36, wherein the detection mode is a maintenance mode detection comprising an alarm detected during maintenance.
39. The system of clause 36, wherein the detection mode is a failure mode detection.
40. The system of clause 39, wherein the controller communicates the failure mode detection facility.
41. The system of clause 36, wherein the detection mode is a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information.
42. The system of clause 36, wherein the detection mode is a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information.
43. The system of clause 27, further comprising the analysis circuit setting the alarm state when the alarm threshold level is exceeded for a alternate input channel in the first group of input channels.
44. The system of clause 43, wherein the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis.
45. The system of clause 27, wherein the alternate routing of input channels comprises a change in a routing collection parameter.
46. The system of clause 45, wherein the routing collection parameter is an increase in sampling rate.
47. The system of clause 45, wherein the routing collection parameter is an increase in the number of channels being sampled.
48. The system of clause 45, wherein the routing collection parameter comprises a burst sampling of at least one of the plurality of input channels.
49. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels and transmits the alarm state across a network to a routing control facility; and
providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel, wherein the data collector automatically executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met.
50. The method of clause 49, wherein the computer-implemented method is deployed locally on the data collector.
51. The method of clause 49, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
52. The method of clause 49, wherein each of the input channels corresponds to a sensor located in the environment.
53. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels and transmits the alarm state across a network to a routing control facility; and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels upon reception of a routing change indication from the routing control facility, wherein the alternate routing of input channels comprise the first input channel and a group of input channels related to the first input channel, wherein the data collector automatically executes the change in routing of the input channels if a communication parameter of the network between the data collector and the routing control facility is not met.
54. The one or more non-transitory computer-readable media of clause 53, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
55. The one or more non-transitory computer-readable media of clause 53, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
56. The one or more non-transitory computer-readable media of clause 53, wherein each of the input channels corresponds to a sensor located in the environment.
57. A monitoring system for data collection in an industrial environment, the system comprising:
a first and second data collector communicatively coupled to a plurality of input channels;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
a data storage structured to store sensor specifications for sensors that correspond to the input channels; a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels;
a communication circuit structured to communicate with a second data collector, wherein the second data collector transmits a state message related to a first input channel from the first route of input channels, and
a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels based on the state message from the second data collector, wherein the alternate routing of input channel comprise the first input channel and a group of input channels related to the first input sensor.
58. The system of clause 57, wherein the system is deployed locally on the data collector.
59. The system of clause 57, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
60. The system of clause 57, wherein each of the input channels corresponds to a sensor located in the environment.
61. The system of clause 57, wherein the set state message transmitted from the second data collector was from a second input channel that is mounted in proximity to the first input channel.
62. The system of clause 57, wherein the set alarm transmitted from the second controller was from a second input sensor that is part of a related group of input sensors comprising the first input sensor.
63. The system of clause 57, wherein the group of input channels is related to the first input channel are at least in part taken from the plurality of input channels not included in the first routing of input channels.
64. The system of clause 57, wherein the alarm state indicates a detection mode.
65. The system of clause 57, wherein the detection mode is an operational mode detection comprising an out-of-range detection.
66. The system of clause 64, wherein the detection mode is a maintenance mode detection comprising an alarm detected during maintenance.
67. The system of clause 64, wherein the detection mode is a failure mode detection.
68. The system of clause 67, wherein the controller communicates the failure mode detection facility.
69. The system of clause 64, wherein the detection mode is a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information.
70. The system of clause 64, wherein the detection mode is a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information.
71. The system of clause 57, further comprising the analysis circuit setting the alarm state when the alarm threshold level is exceeded for a alternate input channel in the first group of input channels.
72. The system of clause 71, wherein the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis.
73. The system of clause 57, wherein the alternate routing of input channels comprises a change in a routing collection parameter.
74. The system of clause 73, wherein the routing collection parameter is an increase in sampling rate.
75. The system of clause 73, wherein the routing collection parameter is an increase in the number of channels being sampled.
76. The system of clause 73, wherein the routing collection parameter comprises a burst sampling of at least one of the plurality of input channels.
77. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a first and second data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels; providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels;
providing a communication circuit structured to communicate with a second data collector, wherein the second data collector transmits a state message related to a first input channel from the first route of input channels, and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels based on the state message from the second data collector, wherein the alternate routing of input channel comprise the first input channel and a group of input channels related to the first input sensor.
78. The method of clause 77, wherein the computer-implemented method is deployed locally on the data collector.
79. The method of clause 77, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
80. The method of clause 77, wherein each of the input channels corresponds to a sensor located in the environment.
81. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a first and second data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels for the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels;
providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels;
providing a communication circuit structured to communicate with a second data collector, wherein the second data collector transmits a state message related to a first input channel from the first route of input channels, and providing a response circuit structured to change the routing of the input channels for data collection from the first routing of input channels to an alternate routing of input channels based on the state message from the second data collector,
wherein the alternate routing of input channel comprise the first input channel and a group of input channels related to the first input sensor.
82. The one or more non-transitory computer-readable media of clause 81, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
83. The one or more non-transitory computer-readable media of clause 81, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
84. The one or more non-transitory computer-readable media of clause 81, wherein each of the input channels corresponds to a sensor located in the environment.
85. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channel, wherein the data acquisition circuit acquires sensor data from a first group of input channels from the plurality of input channels;
a data storage structured to store sensor specifications for sensors that correspond to the input channels;
a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channel; and
a response circuit structured to change the input channels being collected from the first group of input channels to an alternative group of input channels, wherein the alternate group of input channels comprise the first input channel and a group of input channels related to the first input sensor.
86. The system of clause 85, wherein the system is deployed locally on the data collector.
87. The system of clause 85, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
88. The system of clause 85, wherein each of the input channels corresponds to a sensor located in the environment.
89. The system of clause 85, wherein the group of input sensors related to the first input sensor are at least in part taken from the plurality of input sensors not included in the first group of input sensors.
90. The system of clause 85, wherein the first group of input channels is related to the first input channel are at least in part taken from the plurality of input channels not included in the first routing of input channels.
91. The system of clause 85, wherein the alarm state indicates a detection mode.
92. The system of clause 91, wherein the detection mode is an operational mode detection comprising an out-of-range detection.
93. The system of clause 91, wherein the detection mode is a maintenance mode detection comprising an alarm detected during maintenance.
94. The system of clause 91, wherein the detection mode is a failure mode detection.
95. The system of clause 94, wherein the controller communicates the failure mode detection facility.
96. The system of clause 91, wherein the detection mode is a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information.
97. The system of clause 91, wherein the detection mode is a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information.
98. The system of clause 85, further comprising the analysis circuit setting the alarm state when the alarm threshold level is exceeded for a alternate input channel in the first group of input channels.
99. The system of clause 98, wherein the setting of the alarm state for the first input channel and the alternate input channel are determined to be a multiple-instance anomaly detection, wherein the alternate routing of input channels comprises the first input channel and a second input channel, wherein the sensor data from the first input channel and the second input channel contribute to simultaneous data analysis.
100. The system of clause 85, wherein alternative group of input channels comprises a change in a routing collection parameter.
101. The system of clause 100, wherein the routing collection parameter is an increase in sampling rate.
102. The system of clause 100, wherein the routing collection parameter is an increase in the number of channels being sampled.
103. The system of clause 100, wherein the routing collection parameter comprises a burst sampling of at least one of the plurality of input channels.
104. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channel, wherein the data acquisition circuit acquires sensor data from a first group of input channels from the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels;
providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channel; and
providing a response circuit structured to change the input channels being collected from the first group of input channels to an alternative group of input channels, wherein the alternate group of input channels comprise the first input channel and a group of input channels related to the first input sensor.
105. The method of clause 104, wherein the computer-implemented method is deployed locally on the data collector.
106. The method of clause 104, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
107. The method of clause 104, wherein each of the input channels corresponds to a sensor located in the environment.
108. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channel, wherein the data acquisition circuit acquires sensor data from a first group of input channels from the plurality of input channels;
providing a data storage structured to store sensor specifications for sensors that correspond to the input channels;
providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channel; and
providing a response circuit structured to change the input channels being collected from the first group of input channels to an alternative group of input channels, wherein the alternate group of input channels comprise the first input channel and a group of input channels related to the first input sensor.
109. The one or more non-transitory computer-readable media of clause 108, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
110. The one or more non-transitory computer-readable media of clause 108, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
111. The one or more non-transitory computer-readable media of clause 108, wherein each of the input channels corresponds to a sensor located in the environment.
112. A monitoring system for data collection in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels;
a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels; and
a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels, wherein the data collector is configured to switch from a current routing template collection routine to an alternate routing template collection routine based on a setting of an alarm state.
113. The system of clause 112, wherein the system is deployed locally on the data collector.
114. The system of clause 112, wherein the system is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
115. The system of clause 112, wherein each of the input channels corresponds to a sensor located in the environment.
116. The system of clause 112, wherein the setting of the alarm state is based on operational mode routing collection schemes.
117. The system of clause 5, wherein the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power savings operational mode.
118. The system of clause 112, wherein the alarm threshold level is associated with a sensed change to one of the plurality of input channels.
119. The system of clause 118, wherein the sensed change is a failure condition.
120. The system of clause 118, wherein the sensed change is a performance condition.
121. The system of clause 118, wherein the sensed change is a power condition.
122. The system of clause 118, wherein the sensed change is a temperature condition.
123. The system of clause 118, wherein the sensed change is a vibration condition.
124. The system of clause 112, wherein the alarm state indicates a detection mode.
125. The system of clause 124, wherein the detection mode is an operational mode detection comprising an out-of-range detection.
126. The system of clause 124, wherein the detection mode is a maintenance mode detection comprising an alarm detected during maintenance.
127. The system of clause 117, wherein the detection mode is a failure mode detection.
128. The system of clause 117, wherein the detection mode is a power mode detection wherein the alarm state is indicative of a power related limitation data of the anticipated state information.
129. The system of clause 117, wherein the detection mode is a performance mode detection wherein the alarm state is indicative of a high-performance limitation data of the anticipated state information.
130. The system of clause 112, further comprising the analysis circuit setting the alarm state when the alarm threshold level is exceeded for an alternate input channel.
131. The system of clause 130, wherein the setting of the alarm state is determined to be a multiple-instance anomaly detection.
132. The system of clause 112, wherein the alternate routing template comprises a change to an input channel routing collection parameter.
133. The system of clause 132, wherein the routing collection parameter is an increase in sampling rate.
134. The system of clause 133, wherein the routing collection parameter is an increase in the number of channels being sampled.
135. The system of clause 134, wherein the routing collection parameter comprises a burst sampling of at least one of the plurality of input channels.
136. A computer-implemented method for implementing a monitoring system for data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels; and
providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels,
wherein the data collector is configured to switch from a current routing template collection routine to an alternate routing template collection routine based on a setting of an alarm state.
137. The method of clause 136, wherein the computer-implemented method is deployed locally on the data collector.
138. The method of clause 136, wherein the computer-implemented method is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
139. The method of clause 136, wherein each of the input channels corresponds to a sensor located in the environment.
140. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
providing a data collector communicatively coupled to a plurality of input channels;
providing a data storage structured to store a plurality of collector route templates, sensor specifications for sensors that correspond to the input channels, wherein the plurality of collector route templates each comprise a different sensor collection routine;
providing a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of the input channels, wherein the data acquisition circuit acquires sensor data from a first route of input channels; and
providing a data analysis circuit structured to evaluate the sensor data with respect to stored anticipated state information, wherein the anticipated state information comprises an alarm threshold level, and wherein the data analysis circuit sets an alarm state when the alarm threshold level is exceeded for a first input channel in the first group of input channels,
wherein the data collector is configured to switch from a current routing template collection routine to an alternate routing template collection routine based on a setting of an alarm state.
141. The one or more non-transitory computer-readable media of clause 140, wherein the one or more non-transitory computer-readable media is deployed locally on the data collector.
142. The one or more non-transitory computer-readable media of clause 140, wherein the one or more non-transitory computer-readable media is deployed in part locally on the data collector and in part on a remote information technology infrastructure component apart from the collector.
143. The one or more non-transitory computer-readable media of clause 140, wherein each of the input channels corresponds to a sensor located in the environment.
Methods and systems are disclosed herein for a system for data collection in an industrial environment using intelligent management of data collection bands, referred to herein in some cases as smart bands. Smart bands may facilitate intelligent, situational, context-aware collection of data, such as by a data collector (such as any of the wide range of data collector embodiments described throughout this disclosure). Intelligent management of data collection via smart bands may improve various parameters of data collection, as well as parameters of the processes, applications, and products that depend on data collection, such as data quality parameters, consistency parameters, efficiency parameters, comprehensiveness parameters, reliability parameters, effectiveness parameters, storage utilization parameters, yield parameters (including financial yield, output yield, and reduction of adverse events), energy consumption parameters, bandwidth utilization parameters, input/output speed parameters, redundancy parameters, security parameters, safety parameters, interference parameters, signal-to-noise parameters, statistical relevancy parameters, and others. Intelligent management of smart bands may optimize across one or more such parameters, such as based on a weighting of the value of the parameters; for example, a smart band may be managed to provide a given level of redundancy for critical data, while not exceeding a specified level of energy usage. This may include using a variety of optimization techniques described throughout this disclosure and the documents incorporated herein by reference.
In embodiments, such methods and systems for intelligent management of smart bands include an expert system and supporting technology components, services, processes, modules, applications and interfaces, for managing the smart bands (collectively referred to in some cases as a smart band platform 10722), which may include a model-based expert system, a rule-based expert system, an expert system using artificial intelligence (such as a machine learning system, which may include a neural net expert system, a self-organizing map system, a human-supervised machine learning system, a state determination system, a classification system, or other artificial intelligence system), or various hybrids or combinations of any of the above. References to an expert system should be understood to encompass utilization of any one of the foregoing or suitable combinations, except where context indicates otherwise. Intelligent management may be of data collection of various types of data (e.g., vibration data, noise data and other sensor data of the types described throughout this disclosure) for event detection, state detection, and the like. Intelligent management may include managing a plurality of smart bands each directed at supporting an identified application, process or workflow, such as confirming progress toward or alignment with one or more objectives, goals, rules, policies, or guidelines. Intelligent management may also involve managing data collection bands targeted to backing out an unknown variable based on collection of other data (such as based on a model of the behavior of a system that involves the variable), selecting preferred inputs among available inputs (including specifying combinations, fusions, or multiplexing of inputs), and/or specifying an input band among available input bands.
Data collection bands, or smart bands, may include any number of items such as sensors, input channels, data locations, data streams, data protocols, data extraction techniques, data transformation techniques, data loading techniques, data types, frequency of sampling, placement of sensors, static data points, metadata, fusion of data, multiplexing of data, and the like as described herein. Smart band settings, which may be used interchangeably with smart band and data collection band, may describe the configuration and makeup of the smart band, such as by specifying the parameters that define the smart band. For example, data collection bands, or smart bands, may include one or more frequencies to measure. Frequency data may further include at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope, as well as other signal characteristics described throughout this disclosure. Smart bands may include sensors measuring or data regarding one or more wavelengths, one or more spectra, and/or one or more types of data from various sensors and metadata. Smart bands may include one or more sensors or types of sensors of a wide range of types, such as described throughout this disclosure and the documents incorporated by reference herein. Indeed, the sensors described herein may be used in any of the methods or systems described throughout this disclosure. For example, one sensor may be an accelerometer, such as one that measures voltage per G of acceleration (e.g. 100 mV/G, 500 mV/G, 1 V/G, 5 V/G, 10 V/G, and the like). In embodiments, the data collection band circuit may alter the makeup of the subset of the plurality of sensors used in a smart band based on optimizing the responsiveness of the sensor, such as for example choosing an accelerometer better suited for measuring acceleration of a low speed mixer versus one better suited for measuring acceleration of a high speed industrial centrifuge. Choosing may be done intelligently, such as for example with a proximity probe and multiple accelerometers disposed on a centrifuge where while at low speed one accelerometer is used for measuring in the smart band and another is used at high speeds. Accelerometers come in various types, such as piezo-electric crystal, low frequency (e.g. 10V/G), high speed compressors (10 MV/G), MEMS, and the like. In another example, one sensor may be a proximity probe which can be used for sleeve or tilt-pad bearings (e.g. oil bath), or a velocity probe. In yet another example, one sensor may be a solid-state relay (SSR) that is structured to automatically interface with a routed data collector (such as a mobile or portable data collector) to obtain or deliver data. In another example, a mobile or portable data collector may be routed to alter the makeup of the plurality of available sensors, such as by bringing an appropriate accelerometer to a point of sensing, such as on or near a component of a machine. In still another example, one sensor may be a triax probe (e.g. a 100 MV/G triax probe), that in embodiments is used for portable data collection. In some embodiments, of a triax probe, a vertical element on one axis of the probe may have a high frequency response while the ones mounted horizontally may influence the frequency response of the whole triax. In another example, one sensor may be a temperature sensor and may include a probe with a temperature sensor built inside, such as to obtain a bearing temperature. In still additional examples, sensors may be ultrasonic, microphone, touch, capacitive, vibration, acoustic, pressure, strain gauges, thermographic (e.g. camera), imaging (e.g. camera, laser, IR, structured light), a field detector, an EMF meter to measure an AC electromagnetic field, a gaussmeter, a motion detector, a chemical detector, a gas detector, a CBRNE detector, a vibration transducer, a magnetometer, positional, location-based, a velocity sensor, a displacement sensor, a tachometer, a flow sensor, a level sensor, a proximity sensor, a pH sensor, a hygrometer/moisture sensor, a densitometric sensor, an anemometer, a viscometer, or any analog industrial sensor and/or digital industrial sensor. In a further example, sensors may be directed at detecting or measuring ambient noise, such as a sound sensor or microphone, an ultrasound sensor, an acoustic wave sensor, and an optical vibration sensors (e.g. using a camera to see oscillations that produce noise). In still another example, one sensor may be a motion detector.
Data collection bands, or smart bands, may be of or may be configured to encompass one or more frequencies, wavelengths or spectra for particular sensors, for particular groups of sensors, or for combined signals from multiple sensors (such as involving multiplexing or sensor fusion).
Data collection bands, or smart bands, may be of or may be configured to encompass one or more sensors or sensor data (including groups of sensors and combined signals) from one or more pieces of equipment/components, areas of an installation, disparate but interconnected areas of an installation (e.g. a machine assembly line and a boiler room used to power the line), or locations (e.g. a building in Cambridge and a building in Boston). Smart band settings, configurations, instructions, or specifications (collectively referred to herein using any one of those terms) may include where to place a sensor, how frequently to sample a data point or points, the granularity at which a sample is taken (e.g., a number of sampling points per fraction of a second), which sensor of a set of redundant sensors to sample, an average sampling protocol for redundant sensors, and any other aspect that would affect data acquisition.
Within the smart band platform 10722, an expert system, which may comprise a neural net, a model-based system, a rule-based system, a machine learning data analysis circuit and/or a hybrid of any of those, may begin iteration towards convergence on a smart band that is optimized for a particular goal or outcome, such as predicting and managing performance, health, or other characteristics of a piece of equipment, a component, or a system of equipment or components. Based on continuous or periodic analysis of sensor data, as patterns/trends are identified, or outliers appear, or a group of sensor readings begin to change, etc., the expert system may modify its data collection bands intelligently. This may occur by triggering a rule that reflects a model or understanding of system behavior (e.g., recognizing a shift in operating mode that calls for different sensors as velocity of a shaft increases) or it may occur under control of a neural net (either in combination with a rule-based approach or on its own), where inputs are provided such that the neural net over time learns to select appropriate collection modes based on feedback as to successful outcomes (e.g., successful classification of the state of a system, successful prediction, successful operation relative to a metric, or the like). For example, when a new pressure reactor is installed in a chemical processing facility, data from the current data collection band may not accurately predict the state or metric of operation of the system, thus, the machine learning data analysis circuit may begin to iterate to determine if a new data collection band is better at predicting a state. Based on offset system data, such as from a library or other data structure, certain sensors, frequency bands or other smart band members may be used in the smart band initially and data may be collected to assess performance. As the neural net iterates, other sensors/frequency bands may be accessed to determine their relative weight in identifying performance metrics. Over time, a new frequency band may be identified (or a new collection of sensors, a new set of configurations for sensors, or the like) as a better gauge of performance in the system and the expert system may modify its data collection band based on this iteration. For example, perhaps a slightly different or older associated turbine agitator in a chemical reaction facility dampens one or more vibration frequencies while a different frequency is of higher amplitude and present during optimal performance than what was seen in the offset system. In this example, the smart band may be altered from what was suggested by the corresponding offset system to capture the higher amplitude frequency that is present in the current system.
The expert system, in embodiments involving a neural net or other machine learning system, may be seeded and may iterate, such as towards convergence on a smart band, based on feedback and operation parameters, such as described herein. Certain feedback may include utilization measures, efficiency measures (e.g. power or energy utilization, use of storage, use of bandwidth, use of input/output use of perishable materials, use of fuel, and/or financial efficiency), measures of success in prediction or anticipation of states (e.g. avoidance and mitigation of faults), productivity measures (e.g. workflow), yield measures, and profit measures. Certain parameters may include storage parameters (e.g., data storage, fuel storage, storage of inventory and the like), network parameters (e.g., network bandwidth, input/output speeds, network utilization, network cost, network speed, network availability and the like), transmission parameters (e.g., quality of transmission of data, speed of transmission of data, error rates in transmission, cost of transmission and the like), security parameters (e.g., number and/or type of exposure events, vulnerability to attack, data loss, data breach, access parameters, and the like), location and positioning parameters (e.g., location of data collectors, location of workers, location of machines and equipment, location of inventory units, location of parts and materials, location of network access points, location of ingress and egress points, location of landing positions, location of sensor sets, location of network infrastructure, location of power sources and the like), input selection parameters, data combination parameters (e.g., for multiplexing, extraction, transformation, loading, and the like), power parameters, states (e.g. operating modes, availability states, environmental states, fault modes, maintenance modes, anticipated states), events, and equipment specifications. With respect to states, operating modes may include mobility modes (direction, speed, acceleration and the like), type of mobility modes (e.g., rolling, flying, sliding, levitation, hovering, floating, and the like), performance modes (e.g., gears, rotational speeds, heat levels, assembly line speeds, voltage levels, frequency levels, and the like), output modes, fuel conversion modes, resource consumption modes, and financial performance modes (e.g. yield, profitability). Availability states may refer to anticipating conditions that could cause machine to go offline or require backup. Environmental states may refer to ambient temperature, ambient humidity/moisture, ambient pressure, ambient wind/fluid flow, presence of pollution or contaminants, presence of interfering elements (e.g. electrical noise, vibration), power availability, and power quality. Anticipated states may include achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization, an avoidance of a fault condition (e.g. overheating, slow performance, excessive speed, excessive motion, excessive vibration/oscillation, excessive acceleration, expansion/contraction, electrical failure, running out of stored power/fuel, overpressure, excessive radiation/melt down, fire, freezing, failure of fluid flow (e.g. stuck valves, frozen fluids), mechanical failures (e.g. broken component, worn component, faulty coupling, misalignment, asymmetries/deflection, damaged component [e.g. deflection, strain, stress, cracking], imbalances, collisions, jammed elements, and lost or slipping chain or belt), avoidance of a dangerous condition or catastrophic failure, and availability (online status).
The expert system may comprise or be seeded with a model that predicts an outcome or state given a set of data (which may comprise inputs from sensors, such as via a data collector, as well as other data, such as from system components, from external systems and from external data sources). For example, the model may be an operating model for an industrial environment, machine, or workflow. In another example, the model may be for anticipating states, for predicting fault and optimizing maintenance, for self-organizing storage (e.g. on devices, in data pools and/or in the cloud), for optimizing data transport (such as for optimizing network coding, network-condition-sensitive routing, and the like), for optimizing data marketplaces, and the like.
The iteration of the expert system may result in any number of downstream actions based on analysis of data from the smart band. In an embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given a desired goal, such as a specified/threshold output production rate, specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization, an avoidance of a fault condition, an avoidance of a dangerous condition or catastrophic failure, and the like. In embodiments, the adjustments may be based on determining context of an industrial system, such as understanding a type of equipment, its purpose, its typical operating modes, the functional specifications for the equipment, the relationship of the equipment to other features of the environment (including any other systems that provide input to or take input from the equipment), the presence and role of operators (including humans and automated control systems), and ambient or environmental conditions. For example, in order to achieve a profit goal, a pipeline in a refinery may need to operate for a certain amount of time a day and/or at a certain flow rate. The expert system may be seeded with a model for operation of the pipeline in a manner that results in a specified profit goal, such as indicating a given flow rate of material through the pipeline based on the current market sale price for the material and the cost of getting the material into the pipeline. As it acquires data and iterates, the model will predict whether the profit goal will be achieved given the current data. Based on the results of the iteration of the expert system, a recommendation may be made (or a control instruction may be automatically provided) to operate the pipeline at a higher flow rate, to keep it operational for longer or the like. Further, as the system iterates, one or more additional sensors may be sampled in the model to determine if their addition to the smart band would improve predicting a state. In another embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net or other model given a constraint of operation (e.g. meeting a required endpoint (e.g. delivery date, amount, cost, coordination with another system), operating with a limited resource (e.g. power, fuel, battery), storage (e.g. data storage), bandwidth (e.g. local network, p2p, WAN, internet bandwidth, availability, or input/output capacity), authorization (e.g. role-based)), a warranty limitation, a manufacturer's guideline, a maintenance guideline). For example, a constraint of operating a boiler in a refinery is that boiler feedwater must be deaerated; therefore, the boiler must coordinate with the deaerator. In this example, the expert system is seeded with a model for operation of the boiler in coordination with the deareator that results in a specified overall performance. As sensor data from the system is acquired, the expert system may determine that an aspect of one or both of the boiler and aerator must be changed to continue to achieve the specific overall performance. In a further embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given an identified choke point. In still another embodiment, the expert system may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given an off-nominal operation. For example, a reciprocating compressor in a refinery that delivers gases at high pressure may be measured as having an off-nominal operation by sensors that feed their data into an expert system (optionally including a neural net or other machine learning system). As the expert system iterates and receives the off-nominal data, it may predict that the refinery will not achieve a specified goal and will recommend an action, such as taking the reciprocating compressor offline for maintenance. In another embodiment, the expert system may determine that the system should collect more/fewer data points from one or more sensors. For example, an anchor agitator in a pharmaceutical processing plant may be programmed to agitate the contents of a tank until a certain level of viscosity (e.g. as measured in centipoise) is obtained. As the expert system collects data throughout the run indicating an increase in viscosity, the expert system may recommend collecting additional data points to confirm a predicted state in the face of the increased strain on the plant systems from the viscosity. In yet another embodiment, the expert system may determine that the system should change a data storage technique. In still another example, the expert system may determine that the system should change a data presentation mode or manner. In a further embodiment, the expert system may determine that the system should apply one or more filters (low pass, high pass, band pass, etc.) to collected data. In yet a further embodiment, the expert system may determine that the system should collect data from a new smart band/new set of sensors and/or begin measuring a new aspect that the neural net identified itself. For example, various measurements may be made of paddle-type agitator mixers operating in a pharmaceutical plant, such as mixing times, temperature, homogeneous substrate distribution, heat exchange with internal structures and the tank wall or oxygen transfer rate, mechanical stress, forces and torques on agitator vessels and internal structures, and the like. Various sensor data streams may be included in a smart band monitoring these various aspects of the paddle-type agitator mixer, such as a flow meter, a thermometer, and others. As the expert system iterates, perhaps having been seeded with minimal data from during the agitator's run, a new aspect of the operation may become apparent, such as the impact of pH on the state of the run. Thus, a new smart band will be identified by the expert system that includes sensor data from a pH meter. In yet still a further embodiment, the expert system may determine that the system should discontinue collection of data from a smart band/one or more sensors. In another embodiment, the expert system may determine that the system should initiate data collection from a new smart band, such as a new smart band identified by the neural net itself. In yet another embodiment, the expert system may determine that the system should adjust the weights/biases of a model used by the expert system. In still another embodiment, the expert system may determine that the system should remove/re-task under-utilized equipment. For example, a plurality of agitators working with a pump blasting liquid in a pharmaceutical processing plant may be monitored during operation of the plant by the expert system. Through iteration of the expert system seeded with data from a run of the plant with the agitators, the expert system may predict that a state will be achieved even if one or more agitators are taken out of service.
In embodiments, a monitoring system for data collection in an industrial environment may include a plurality of input sensors, such as any of those described herein, communicatively coupled to a data collector having a controller. The monitoring system may include a data collection band circuit structured to determine at least one subset of the plurality of sensors from which to process output data. The monitoring system may also include a machine learning data analysis circuit structured to receive output data from the at least one subset of the plurality of sensors and learn received output data patterns indicative of a state. In some embodiments, the data collection band circuit may alter the at least one subset of the plurality of sensors, or an aspect thereof, based on one or more of the learned received output data patterns and the state. In certain embodiments, the machine learning data analysis circuit is seeded with a model that enables it to learn data patterns. The model may be a physical model, an operational model, a system model and the like. In other embodiments, the machine learning data analysis circuit is structured for deep learning wherein input data is fed to the circuit with no or minimal seeding and the machine learning data analysis circuit learns based on output feedback. For example, a static mixer in a chemical processing plant producing polymers may be used to facilitate the polymerization reaction. The static mixer may employ turbulent or laminar flow in its operation Minimal data, such as heat transfer, velocity of flow out of the mixer, Reynolds number or pressure drop, acquired during the operation of the static mixer may be fed into the expert system which may iterate towards a prediction based on initial feedback (e.g. viscosity of the polymer, color of the polymer, reactivity of the polymer).
There may be a balance of multiple goals/guidelines in the management of smart bands by the expert system. For example, a repair and maintenance organization (RMO) may have operating parameters designed for maintenance of a storage tank in a refinery, while the owner of the refinery may have particular operating parameters for the storage tank that are designed for meeting a production goal. These goals, in this example relating to a maintenance goal or a production output, may be tracked by a different data collection bands. For example, maintenance of a storage tank may be tracked by sensors including a vibration transducer and a strain gauge while the production goal of a storage tank may be tracked by sensors including a temperature sensor and a flow meter. The expert system may (optionally using a neural net, machine learning system, deep learning system, or the like, which may occur under supervision by one or more supervisors (human or automated) intelligently manage bands aligned with different goals and assign weights, parameter modifications, or recommendations based on a factor, such as a bias towards one goal or a compromise to allow better alignment with all goals being tracked, for example. Compromises among the goals delivered to the expert system may be based on one or more hierarchies or rules relating to the authority, role, criticality, or the like of the applicable goals. In embodiments, compromises among goals may be optimized using machine learning, such as a neural net, deep learning system, or other artificial intelligence system as described throughout this disclosure. In one illustrative example, in a chemical processing plant where a gas-powered agitator is operating, the expert system may manage multiple smart bands, such as one directed to detecting the operational status of the gas-powered agitator, one directed at identifying a probability of hitting a production goal, and one directed at determining if the operation of the gas-powered agitator is meeting a fuel efficiency goal. Each of these smart bands may be populated with different sensors or data from different sensors (e.g. a vibration transducer to indicate operational status, a flow meter to indicate production goal, and a fuel gauge to indicate a fuel efficiency) whose output data are indicative of an aspect of the particular goal. Where a single sensor or a set of sensors is helpful for more than one goal, overlapping smart bands (having some sensors in common and other sensors not in common) may take input from that sensor or set of sensors, as managed by the smart band platform 10722. If there are constraints on data collection (such as due to power limitations, storage limitations, bandwidth limitations, input/output processing capabilities, or the like), a rule may indicate that one goal (e.g., a fuel utilization goal or a pollution reduction goal that is mandated by law or regulation) takes precedence, such that the data collection for the smart bands associated with that goal are maintained as others are paused or shut down. Management of prioritization of goals may be hierarchical or may occur by machine learning. The expert system may be seeded with models, or may not be seeded at all, in iterating towards a predicted state (i.e. meeting the goal) given the current data it has acquired. In this example, during operation of the gas-powered agitator, the plant owner may decide to bias the system towards fuel efficiency. All of the bands may still be monitored, but as the expert system iterates and predicts that the system will not meet or is not meeting a particular goal and then offers recommended changes directed at increasing the chance of meeting the goal, the plant owner may structure the system with a bias towards fuel efficiency so that the recommended changes to parameters affecting fuel efficiency are made in favor of making other recommended changes.
In embodiments, the expert system may continue iterating in a deep-learning fashion to arrive at a single smart band, after being seeded with more than one smart band, that optimizes meeting more than one goal. For example, there may be multiple goals tracked for a thermic heating system in a chemical processing or a food processing plant, such as thermal efficiency and economic efficiency. Thermal efficiency for the thermic heating system may be expressed by comparing BTUs put into the system, which can be obtained by knowing the amount of and quality of the fuel being used, and the BTUs out of the system, which is calculated using the flow out of the system and the temperature differential of materials in and out of the system. Economic efficiency of the thermic heating system may be expressed as the ratio between costs to run the system, including fuel, labor, materials and services, and energy output from the system for a period of time. Data used to track thermal efficiency may include data from a flow meter, quality data point(s), and a thermometer, and data used to track economic efficiency may be an energy output from the system (e.g. kWh) and costs data. These data may be used in smart bands by the expert system to predict states, however, the expert system may iterate towards a smart band that is optimized to predict states related to both thermal and economic efficiency. The new smart band may include data used previously in the individual smart bands but may also use new data from different sensors or data sources. In embodiments, the expert system may be seeded with a plurality of smart bands and iterate to predict various states, but may also iterate towards reducing the number of smart bands needed to predict the same set of states.
Iteration of the expert system may be governed by rules, in some embodiments. For example, the expert system may be structured to collect data for seeding at a pre-determined frequency. The expert system may be structured to iterate at least a number of times, such as when a new component/equipment/fuel source is added, when a sensor goes off-line, or as standard practice. For example, when a sensor measuring the rotation of a stirrer in a food processing line goes off-line and the expert system begins acquiring data from a new sensor measuring the same data points, the expert system may be structured to iterate for a number of times before the state is utilized in or allowed to affect any downstream actions. The expert system may be structured to train off-line or train in situ/online. The expert system may be structured to include static and/or manually input data in its smart bands. For example, an expert system managing smart bands associated with a mixer in a food processing plant may be structured to iterate towards predicting a duration of mixing before the food being processed achieves a particular viscosity, wherein the smart band includes data regarding the speed of the mixer, temperature of its contents, viscometric measurements and the required endpoint for viscosity and temperature of the food. The expert system may be structured to include a minimum/maximum number of variables.
In embodiments, the expert system may be overruled. In embodiments, the expert system may revert to prior band settings, such as in the event the expert system fails, such as if a neural network fails in a neural net expert system, if uncertainty is too high in a model-based system, if the system is unable to resolve conflicting rules in rule-based system, or the system cannot converge on a solution in any of the foregoing. For example, sensor data on an irrigation system used by the expert system in a smart band may indicate a massive leak in the field, but visual inspection, such as by a drone, indicates no such leak. In this event, the expert system will revert to an original smart band for seeding the expert system. In another example, one or more point sensors on an industrial pressure cooker indicates imminent failure in a seal, but the data collection band that the expert system converged to with a weighting towards a performance metric did not identify the failure. In this event, the smart band will revert to an original setting or a version of the smart band that would have also identified the imminent failure of the pressure cooker seal. In embodiments, the expert system may change smart band settings in the event that a new component is added that makes the system closer to a different offset system. For example, a vacuum distillation unit is added to an oil & gas refinery to distill naphthalene, but the current smart band settings for the expert system are derived from a refinery that distills kerosene. In this example, a data structure with smart band settings for various offset systems may be searched for a system that is more closely matched to the current system. When a new offset system is identified as more closely matched, such as one that also distill naphthalene, the new smart band settings (e.g. which sensors to use, where to place them, how frequently to sample, what static data points are needed, etc. as described herein) are used to seed the expert system to iterate towards predicting a state for the system. In embodiments, the expert system may change smart band settings in the event that a new set of offset data is available from a third-party library. For example, a pharmaceutical processing plant may have optimized a catalytic reactor to operate in a highly efficient way and deposited the smart band settings in a data structure. The data structure may be continuously scanned for new smart bands that better aid in monitoring catalytic reactions and thus, result in optimizing the operation of the reactor.
In embodiments, the expert system may be used to uncover unknown variables. For example, the expert system may iterate to identify a missing variable to be used for further iterations, such as further neural net iterations. For example, an under-utilized tank in a legacy condensate/make-up water system of a power station may have an unknown capacity because it is inaccessible and no documentation exists on the tank. Various aspects of the tank may be measured by a swarm of sensors to arrive at an estimated volume (e.g. flow into a downstream space, duration of a dye traced solution to work through the system), then that volume can be fed into the neural net as a new variable in the smart band.
In embodiments, the location of expert system node locations may be on a machine, on a data collector (or a group of them), in a network infrastructure (enterprise or other), or in the cloud. In embodiments, there may be distributed neurons across nodes (e.g. machine, data collector, network, cloud).
Referring to
The monitoring system 10700 may keep or modify operational parameters of an item of equipment in the environment based on the determined state. The controller 10706 may adjust the weighting of the machine learning data analysis circuit 10712 based on the learned received output data patterns 10718 or the state. The controller 10706 may collect more/fewer data points from one or more members of the at least one subset of plurality of sensors 10702 based on the learned received output data patterns 10718 or the state. The controller 10706 may change a data storage technique for the output data 10710 based on the learned received output data patterns 10718 or the state. The controller 10706 may change a data presentation mode or manner based on the learned received output data patterns 10718 or the state. The controller 10706 may apply one or more filters to the output data 10710. The controller 10706 may identify a new data collection band circuit 10708 based on one or more of the learned received output data patterns 10718 and the state. The controller 10706 may adjust the weights/biases of the machine learning data analysis circuit 10712, such as in response to the learned received output data patterns 10718, in response to the accuracy of the prediction of an anticipated state by the machine learning data analysis circuit, in response to the accuracy of a classification of a state by the machine learning data analysis circuit, and the like. The monitoring device 10700 may remove or re-task under-utilized equipment based on one or more of the learned received output data patterns 10718 and the state. The machine learning data analysis circuit 10712 may include a neural network expert system. At least one subset of the plurality of sensors measures vibration and noise data. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of progress/alignment with one or more goals/guidelines, wherein progress/alignment of each goal/guideline may be determined by a different subset of the plurality of sensors. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of an unknown variable. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of a preferred input among available inputs. The machine learning data analysis circuit 10712 may be structured to learn received output data patterns 10718 indicative of a preferred input data collection band among available input data collection bands. The machine learning data analysis circuit 10712 may be disposed in part on a machine, on one or more data collectors, in network infrastructure, in the cloud, or any combination thereof.
In embodiments, a monitoring device for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a controller 10706, the controller 10706 including a data collection band circuit 10708 structured to determine at least one subset of the plurality of sensors 10702 from which to process output data 10710; and a machine learning data analysis circuit 10712 structured to receive output data from the at least one subset of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters an aspect of the at least one subset of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state. The aspect that the data collection band circuit 10708 alters is a number or a frequency of data points collected from one or more members of the at least one subset of plurality of sensors 10702. The aspect that the data collection band circuit 10708 alters is a bandwidth parameter, a timing parameter, a frequency range, a granularity of collection of sensor data, a storage parameter for the collected data, and the like.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the data collection band circuit 10708 alters the at least one of the plurality of sensors 10702 when the learned received output data pattern 10718 does not reliably predict the state.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the data collector 10704 collects more or fewer data points from the at least one of the plurality of sensors 10702 based on the learned received output data patterns 10718 or the state.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data 10710 patterns indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 changes a data storage technique for the output data 10710 based on the learned received output data patterns 10718 or the state.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 changes a data presentation mode or manner based on the learned received output data patterns 10718 or the state.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 identifies a new data collection band circuit 10708 based on one or more of the learned received output data patterns 10718 and the state.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the controller 10706 adjusts the weights/biases of the machine learning data analysis circuit 10712. The adjustment may be in response to the learned received output data patterns, in response to the accuracy of the prediction of an anticipated state by the machine learning data analysis circuit, in response to the accuracy of a classification of a state by the machine learning data analysis circuit, and the like.
In an embodiment, a monitoring system 10700 for data collection in an industrial environment may include a plurality of input sensors 10702 communicatively coupled to a data collector 10704 having a controller 10706, a data collection band circuit 10708 structured to determine at least one collection parameter for at least one of the plurality of sensors 10702 from which to process output data 10710, and a machine learning data analysis circuit 10712 structured to receive output data 10710 from the at least one of the plurality of sensors 10702 and learn received output data patterns 10718 indicative of a state, wherein the data collection band circuit 10708 alters the at least one collection parameter for the at least one of the plurality of sensors 10702 based on one or more of the learned received output data patterns 10718 and the state, and wherein the machine learning data analysis circuit 10712 is structured to learn received output data patterns 10718 indicative of progress or alignment with one or more goals or guidelines.
1. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state.
2. The system of clause 1, wherein the state corresponds to an outcome relating to a machine in the environment.
3. The system of clause 1, wherein the state corresponds to an anticipated outcome relating to a machine in the environment.
4. The system of clause 1, wherein the state corresponds to an outcome relating to a process in the environment.
5. The system of clause 1, wherein the state corresponds to an anticipated outcome relating to a process in the environment.
6. The system of clause 1, wherein the collection parameter is a bandwidth parameter.
7. The system of clause 1, wherein the collection parameter is used to govern the multiplexing of a plurality of the input sensors.
8. The system of clause 1, wherein the collection parameter is a timing parameter.
9. The system of clause 1, wherein the collection parameter relates to a frequency range.
10. The system of clause 1, wherein the collection parameter relates to the granularity of collection of sensor data.
11. The system of clause 1, wherein the collection parameter is a storage parameter for the collected data.
12. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model.
13. The system of clause 12, wherein the model is a physical model, an operational model, or a system model.
14. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns based on the state.
15. The system of clause 1, wherein the data collection band circuit alters the subset of the plurality of sensors when the learned received output data pattern does not reliably predict the state.
16. The system of clause 15, wherein altering the at least one subset comprises discontinuing collection of data from the at least one subset.
17. The system of clause 1, wherein the monitoring system keeps or modifies operational parameters of an item of equipment in the environment based on the determined state.
18. The system of clause 1, wherein the controller adjusts the weighting of the machine learning data analysis circuit based on the learned received output data patterns or the state.
19. The system of clause 1, wherein the controller collects more/fewer data points from one or more members of the at least one subset of plurality of sensors based on the learned received output data patterns or the state.
20. The system of clause 1, wherein the controller changes a data storage technique for the output data based on the learned received output data patterns or the state.
21. The system of clause 1, wherein the controller changes a data presentation mode or manner based on the learned received output data patterns or the state.
22. The system of clause 1, wherein the controller applies one or more filters to the output data.
23. The system of clause 1, wherein the controller identifies a new data collection band circuit based on one or more of the learned received output data patterns and the state.
24. The system of clause 1, wherein the controller adjusts the weights/biases of the machine learning data analysis circuit.
25. The system of clause 24, wherein the adjustment is in response to the learned received output data patterns.
26. The system of clause 24, wherein the adjustment is in response to the accuracy of the prediction of an anticipated state by the machine learning data analysis circuit.
27. The system of clause 24, wherein the adjustment is in response to the accuracy of a classification of a state by the machine learning data analysis circuit.
28. The system of clause 1, wherein the monitoring device removes/re-tasks under-utilized equipment based on one or more of the learned received output data patterns and the state.
29. The system of clause 1, wherein the machine learning data analysis circuit comprises a neural network expert system.
30. The system of clause 1, wherein the at least one subset of the plurality of sensors measure vibration and noise data.
31. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of progress/alignment with one or more goals/guidelines.
32. The system of clause 31, wherein progress/alignment of each goal/guideline is determined by a different subset of the plurality of sensors.
33. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of an unknown variable.
34. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of a preferred input among available inputs.
35. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of a preferred input data collection band among available input data collection bands.
36. The system of clause 1, wherein the machine learning data analysis circuit is disposed in part on a machine, on one or more data collectors, in network infrastructure, in the cloud, or any combination thereof.
37. A monitoring device for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller, the controller comprising:
a data collection band circuit structured to determine at least one subset of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one subset of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters an aspect of the at least one subset of the plurality of sensors based on one or more of the learned received output data patterns and the state.
38. The system of clause 37, wherein the aspect that the data collection band circuit alters is a number of data points collected from one or more members of the at least one subset of plurality of sensors.
39. The system of clause 37, wherein the aspect that the data collection band circuit alters is a frequency of data points collected from one or more members of the at least one subset of plurality of sensors.
40. The system of clause 37, wherein the aspect that the data collection band circuit alters is a bandwidth parameter.
41. The system of clause 37, wherein the aspect that the data collection band circuit alters is a timing parameter.
42. The system of clause 37, wherein the aspect that the data collection band circuit alters relates to a frequency range.
43. The system of clause 37, wherein the aspect that the data collection band circuit alters relates to the granularity of collection of sensor data.
44. The system of clause 37, wherein the collection parameter is a storage parameter for the collected data.
45. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the data collection band circuit alters the at least one of the plurality of sensors when the learned received output data pattern does not reliably predict the state.
46. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the data collector collects more or fewer data points from the at least one of the plurality of sensors based on the learned received output data patterns or the state.
47. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the controller changes a data storage technique for the output data based on the learned received output data patterns or the state.
48. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the controller changes a data presentation mode or manner based on the learned received output data patterns or the state.
49. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the controller identifies a new data collection band circuit based on one or more of the learned received output data patterns and the state.
50. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the controller adjusts the weights/biases of the machine learning data analysis circuit.
51. The system of clause 50, wherein the adjustment is in response to the learned received output data patterns
52. The system of clause 50, wherein the adjustment is in response to the accuracy of the prediction of an anticipated state by the machine learning data analysis circuit.
53. The system of clause 50, wherein the adjustment is in response to the accuracy of a classification of a state by the machine learning data analysis circuit.
54. A monitoring system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a data collector having a controller;
a data collection band circuit structured to determine at least one collection parameter for at least one of the plurality of sensors from which to process output data; and
a machine learning data analysis circuit structured to receive output data from the at least one of the plurality of sensors and learn received output data patterns indicative of a state,
wherein the data collection band circuit alters the at least one collection parameter for the at least one of the plurality of sensors based on one or more of the learned received output data patterns and the state, and wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of progress or alignment with one or more goals or guidelines.
As described elsewhere herein, an expert system in an industrial environment may use sensor data to make predictions about outcomes or states of the environment or items in the environment. Data collection may be of various types of data (e.g., vibration data, noise data and other sensor data of the types described throughout this disclosure) for event detection, state detection, and the like. For example, the expert system may utilize ambient noise, or the overall sound environment of the area and/or overall vibration of the device of interest, optionally in conjunction with other sensor data, in detecting or predicting events or states. For example, a reciprocating compressor in a refinery, which may generate its own vibration, may also have an ambient vibration through contact with other aspects of the system.
In embodiments, all three of ambient noise, local noise and vibration noise, including various subsets thereof and combinations with other types of data, may be organized into large data sets, along with measured results, that are processed by a “deep learning” machine/expert system that learns to predict one or more states (e.g., maintenance, failure, or operational) or overall outcomes, such as by learning from human supervision or from other feedback, such as feedback from one or more of the systems described throughout this disclosure and the documents incorporated by reference herein.
Throughout this disclosure, various examples will involve machines, components, equipment, assemblies, and the like, and it should be understood that the disclosure could apply to any of the aforementioned. Elements of these machines operating in an industrial environment (e.g. rotating elements, reciprocating elements, swinging elements, flexing elements, flowing elements, suspending elements, floating elements, bouncing elements, bearing elements, etc.) may generate vibrations that may be of a specific frequency and/or amplitude typical of the element when the element is in a given operating condition or state (e.g., a normal mode of operation of a machine at a given speed, in a given gear, or the like). Changes in a parameter of the vibration may be indicative or predictive of a state or outcome of the machine. Various sensors may be useful in measuring vibration, such as accelerometers, velocity transducers, imaging sensors, acoustic sensors, and displacement probes, which may collectively be known as vibration sensors. Vibration sensors may be mounted to the machine, such as permanently or temporarily (e.g. adhesive, hook-and-loop, or magnetic attachment), or may be disposed on a mobile or portable data collector. Sensed conditions may be compared to historical data to identify or predict a state, condition or outcome. Typical faults that can be identified using vibration analysis include machine out of balance, machine out of alignment, resonance, bent shafts, gear mesh disturbances, blade pass disturbances, vane pass disturbances, recirculation & cavitation, motor faults (rotor & stator), bearing failures, mechanical looseness, critical machine speeds, and the like, 1 as well as excessive friction, clutch slipping, belt problems, suspension and shock absorption problems, valve and other fluid leaks, under-pressure states in lubrication and other fluid systems, overheating (such as due to many of the above), blockage or freezing of engagement of mechanical systems, interference effects, and other faults described throughout this disclosure and in the documents incorporated by reference.
Given that machines are frequently found adjacent to or working in concert with other machinery, measuring the vibration of the machine may be complicated by the presence of various noise components in the environment or associated vibrations that the machine may be subjected to. Indeed, the ambient and/or local environment may have its own vibration and/or noise pattern that may be known. In embodiments, the combination of vibration data with ambient and/or local noise or other ambient sensed conditions may form its own pattern, as will be further described herein.
In embodiments, measuring vibration noise may involve one or more vibration sensors on or in a machine to measure vibration noise of the machine that occurs continuously or periodically. Analysis of the vibration noise may be performed, such as filtering, signal conditioning, spectral analysis, trend analysis, and the like. Analysis may be performed on aggregate or individual sensor measurements to isolate vibration noise of equipment to obtain a characteristic vibration, vibration pattern or “vibration fingerprint” of the machine. The vibration fingerprints may be stored in a data structure, or library, of vibration fingerprints. The vibration fingerprints may include frequencies, spectra (i.e. frequency vs. amplitude), velocities, peak locations, wave peak shapes, waveform shapes, wave envelope shapes, accelerations, phase information, phase shifts (including complex phase measurements) and the like. Vibration fingerprints may be stored in the library in association with a parameter by which it may be searched or sorted. The parameters may include a brand or type of machine/component/equipment, location of sensor(s) attachment or placement, duty cycle of the equipment/machine, load sharing of the equipment/machine, dynamic interactions with other devices, RPM, flow rate, pressure, other vibration driving characteristic, voltage of line power, age of equipment, time of operation, known neighboring equipment, associated auxiliary equipment/components, size of space equipment is in, material of platform for equipment, heat flux, magnetic fields, electrical fields, currents, voltage, capacitance, inductance, aspect of a product, and combinations (e.g., simple ratios) of the same. Vibration fingerprints may be obtained for machines under normal operation or for other periods of operation (e.g. off-nominal operation, malfunction, maintenance needed, faulty component, incorrect parameters of operation, other conditions, etc.) and can be stored in the library for comparison to current data. The library of vibration fingerprints may be stored as indicators with associated predictions, states, outcomes and/or events. Trend analysis data of measured vibration fingerprints can indicate time between maintenance events/failure events.
In embodiments, vibration noise may be used by the expert system to confirm the status of a machine, such as a favorable operation, a production rate, a generation rate, an operational efficiency, a financial efficiency (e.g. output per cost), a power efficiency, and the like. In embodiments, the expert system may make a comparison of the vibration noise with a stored vibration fingerprint. In other embodiments, the expert system may be seeded with vibration noise and initial feedback on states and outcomes in order to learn to predict other states and outcomes. For example, a center pivot irrigation system may be remotely monitored by attached vibration sensors to provide a measured vibration noise that can be compared to a library of vibration fingerprints to confirm that the system is operating normally. If the system is not operating normally, the expert system may automatically dispatch a field crew or drone to investigate. In another example of a vacuum distillation unit in a refinery, the vibration noise may be compared, such as by the expert system, to stored vibration fingerprints in a library to confirm a production rate of diesel. In a further example, the expert system may be seeded with vibration noise for a pipeline under conditions of a normal production rate and as the expert system iterates with current data (e.g. altered vibration noise, and possibly other altered parameters), it may predict that the production rate has increased as caused by the alterations. Measurements may be continually analyzed in this way to remotely monitor operation.
In embodiments, vibration noise may be compared, such as by the expert system, to stored vibration fingerprints and associated states and outcomes in the library, or alternatively, may be used to seed an expert system to predict when maintenance is required (e.g. off-nominal measurement, artifacts in signal, etc.), such as when vibration noise is matched to a condition when the equipment/component required maintenance, vibration noise exceeds a threshold/limit, vibration noise exceeds a threshold/limit or matches a library vibration fingerprint together with one or more additional parameters, as described herein. For example, when the vibration fingerprint from a turbine agitator in a pharmaceutical processing plant matches a vibration fingerprint for a turbine agitator when it required a replacement bearing, the expert system may cause an action to occur, such as immediately shutting down the agitator or scheduling its shutdown and maintenance.
In embodiments, vibration noise may be compared, such as by the expert system, to stored vibration fingerprints and associated states and outcomes in the library, or alternatively, may be used to seed an expert system to predict a failure or an imminent failure. For example, vibration noise from a gas agitator in a pharmaceutical processing plant may be matched to a condition when the agitator previously failed or was about to fail. In this example, the expert system may immediately shut down the agitator, schedule its shutdown, or cause a backup agitator to come online. In another example, vibration noise from a pump blasting liquid agitator in a chemical processing plant may exceed a threshold or limit and the expert system may cause an investigation into the cause of the excess vibration noise, shut down the agitator, or the like. In another example, vibration noise from an anchor agitator in a pharmaceutical processing plant may exceed a threshold/limit or match a library vibration fingerprint together with one or more additional parameters (see parameters herein), such as a decreased flow rate, increased temperature, or the like. Using vibration noise taken together with the parameters, the expert system may more reliably predict the failure or imminent failure.
In embodiments, vibration noise may be compared, such as by the expert system, to stored vibration fingerprints and associated states and outcomes in the library, or alternatively, may be used to seed an expert system to predict or diagnose a problem (e.g. unbalanced, misaligned, worn or damaged) with the equipment or an external source contributing vibration noise to the equipment. For example, when the vibration noise from a paddle-type agitator mixer matches a vibration fingerprint from a prior imbalance, the expert system may immediately shut down the mixer.
In embodiments, when the expert system makes a prediction of an outcome or state using vibration noise, the expert system may perform a downstream action, or cause it to be performed. Downstream actions may include triggering an alert of a failure, imminent failure, or maintenance event, shutting down equipment/component, initiating maintenance/lubrication/alignment, deploying a field technician, recommending a vibration absorption/dampening device, modifying a process to utilize backup equipment/component, modifying a process to preserve products/reactants, etc., generating/modifying a maintenance schedule, coupling the vibration fingerprint with duty cycle of the equipment, RPM, flow rate, pressure, temperature or other vibration-driving characteristic to obtain equipment/component status and generate a report, and the like. For example, vibration noise for a catalytic reactor in a chemical processing plant may be matched to a condition when the catalytic reactor required maintenance. Based on this predicted state of required maintenance, the expert system may deploy a field technician to perform the maintenance.
In embodiments, the library may be updated if a changed parameter resulted in a new vibration fingerprint or if a predicted outcome or state did not occur in the absence of mitigation. In embodiments, the library may be updated if a vibration fingerprint was associated with an alternative state than what was predicted by the library. The update may occur after just one time that the state that actually occurred did not match the predicted state from the library. In other embodiments, it may occur after a threshold number of times. In embodiments, the library may be updated to apply one or more rules for comparison, such as rules that govern how many parameters to match along with the vibration fingerprint, or the standard deviation for the match in order to accept the predicted outcome.
In embodiments, vibration noise may be compared, such as by the expert system, to stored vibration fingerprints and associated states and outcomes in the library, or alternatively, may be used to seed an expert system to determine if a change in a system parameter external or internal to the machine has an effect on its intrinsic operation. In embodiments, a change in one or more of a temperature, flow rate, materials in use, duration of use, power source, installation, or other parameter (see parameters above) may alter the vibration fingerprint of a machine. For example, in a pressure reactor in a chemical processing plant, the flow rate and a reactant may be changed. The changes may alter the vibration fingerprint of the machine such that the vibration fingerprint stored in the library for normal operation is no longer correct.
Ambient noise, or the overall sound environment of the area and/or overall vibration of the device of interest, optionally in conjunction with other ambient sensed conditions, may be used in detecting or predicting events, outcomes or states. Ambient noise may be measured by a microphone, ultrasound sensors, acoustic wave sensors, optical vibration sensors (e.g. using a camera to see oscillations that produce noise), or “deep learning” neural networks involving various sensor arrays that learn, using large data sets, to identify patterns, sounds types, noise types, etc. In an embodiment, the ambient sensed condition may relate to motion detection. For example, the motion may be a platform motion (e.g., vehicle, oil platform, suspended platform on land, etc.) or an object motion (e.g. moving equipment, people, robots, parts (e.g., fan blades or turbine blades), etc.). In an embodiment, the ambient sensed condition may be sensed by imaging, such as to detect a location and nature of various machines, equipment and other objects, such as ones that might impact local vibration. In an embodiment, the ambient sensed condition may be sensed by thermal detection and imaging (e.g., for presence of people; presence of heat sources that may affect performance parameters, etc.). In an embodiment, the ambient sensed condition may be sensed by field detection (e.g. electrical, magnetic, etc.). In an embodiment, the ambient sensed condition may be sensed by chemical detection (e.g. smoke, other conditions). Any sensor data may be used by the expert system to provide an ambient sensed condition for analysis along with the vibration fingerprint to predict an outcome, event, or state. For example, an ambient sensed condition near a stirrer or mixer in a food processing plant may be the operation of a space heater during winter months, wherein the ambient sensed condition may include an ambient noise and an ambient temperature.
In an aspect, local noise may be the noise or vibration environment which is ambient, but known to be locally generated. The expert system may filter out ambient noise, employ common mode noise removal, and/or physically isolate the sensing environment.
In embodiments, a system for data collection in an industrial environment may use ambient, local and vibration noise for prediction of outcomes, events, and states. A library may be populated with each of the three noise types for various conditions (e.g. start up, shut down, normal operation, other periods of operation as described elsewhere herein). In other embodiments, the library may be populated with noise patterns representing the aggregate ambient, local, and/or vibration noise. Analysis (e.g. filtering, signal conditioning, spectral analysis, trend analysis) may be performed on the aggregate noise to obtain a characteristic noise pattern and identify changes in noise pattern as possible indicators of a changed condition. A library of noise patterns may be generated with established vibration fingerprints and local and ambient noise that can be sorted by a parameter (see parameters herein), or other parameters/features of the local and ambient environment (e.g. company type, industry type, products, robotic handling unit present/not present, operating environment, flow rates, production rates, brand or type of auxiliary equipment (e.g. filters, seals, coupled machinery)). The library of noise patterns may be used by an expert system, such as one with machine learning capacity, to confirm a status of a machine, predict when maintenance is required (e.g. off-nominal measurement, artifacts in signal), predict a failure or an imminent failure, predict/diagnose a problem, and the like.
Based on a current noise pattern, the library may be consulted or used to seed an expert system to predict an outcome, event, or state based on the noise pattern. Based on the prediction, the expert system may one or more of trigger an alert of a failure, imminent failure, or maintenance event, shut down equipment/component/line, initiate maintenance/lubrication/alignment, deploy a field technician, recommend a vibration absorption/dampening device, modify a process to utilize backup equipment/component, modify a process to preserve products/reactants, etc., generate/modify a maintenance schedule, or the like.
For example, a noise pattern for a thermic heating system in a pharmaceutical plant or cooking system may include local, ambient, and vibration noise. The ambient noise may be a result of, for example, various pumps to pump fuel into the system. Local noise may be a result of a local security camera chirping with every detection of motion. Vibration noise may result from the combustion machinery used to heat the thermal fluid. These noise sources may form a noise pattern which may be associated with a state of the thermic system. The noise pattern and associated state may be stored in a library. An expert system used to monitor the state of the thermic heating system may be seeded with noise patterns and associated states from the library. As current data are received into the expert system, it may predict a state based on having learned noise patterns and associated states.
In another example, a noise pattern for boiler feed water in a refinery may include local and ambient noise. The local noise may be attributed to the operation of, for example, a feed pump feeding the feed water into a steam drum. The ambient noise may be attributed to nearby fans. These noise sources may form a noise pattern which may be associated with a state of the boiler feed water. The noise pattern and associated state may be stored in a library. An expert system used to monitor the state of the boiler may be seeded with noise patterns and associated states from the library. As current data are received into the expert system, it may predict a state based on having learned noise patterns and associated states.
In yet another example, a noise pattern for a storage tank in a refinery may include local, ambient, and vibration noise. The ambient noise may be a result of, for example, a pump that pumps a product into the tank. Local noise may be a result of a fan ventilating the tank room. Vibration noise may result from line noise of a power supply into the storage tank. These noise sources may form a noise pattern which may be associated with a state of the storage tank. The noise pattern and associated state may be stored in a library. An expert system used to monitor the state of the storage tank may be seeded with noise patterns and associated states from the library. As current data are received into the expert system, it may predict a state based on having learned noise patterns and associated states.
In another example, a noise pattern for condensate/make-up water system in a power station may include vibration and ambient noise. The ambient noise may be attributed to nearby fans. The vibration noise may be attributed to the operation of the condenser. These noise sources may form a noise pattern which may be associated with a state of the condensate/make-up water system. The noise pattern and associated state may be stored in a library. An expert system used to monitor the state of the condensate/make-up water system may be seeded with noise patterns and associated states from the library. As current data are received into the expert system, it may predict a state based on having learned noise patterns and associated states.
A library of noise patterns may be updated if a changed parameter resulted in a new noise pattern or if a predicted outcome or state did not occur in the absence of mitigation of a diagnosed problem. A library of noise patterns may be updated if a noise pattern resulted in an alternative state than what was predicted by the library. The update may occur after just one time that the state that actually occurred did not match the predicted state from the library. In other embodiments, it may occur after a threshold number of times. In embodiments, the library may be updated to apply one or more rules for comparison, such as rules that govern how many parameters to match along with the noise pattern, or the standard deviation for the match in order to accept the predicted outcome. For example, a baffle may be replaced in a static agitator in a pharmaceutical processing plant which may result in a changed noise pattern. In another example, as the seal on a pressure cooker in a food processing plant ages, the noise pattern associated with the pressure cooker may change.
In embodiments, the library of vibration fingerprints, noise sources and/or noise patterns may be available for subscription. The libraries may be used in offset systems to improve operation of the local system. Subscribers may subscribe at any level (e.g. component, machinery, installation, etc.) in order to access data that would normally not be available to them, such as because it is from a competitor, or is from an installation of the machinery in a different industry not typically considered. Subscribers may search on indicators/predictors based on or filtered by system conditions, or update an indicator/predictor with proprietary data to customize the library. The library may further include parameters and metadata auto-generated by deployed sensors throughout an installation, onboard diagnostic systems and instrumentation and sensors, ambient sensors in the environment, sensors (e.g. in flexible sets) that can be put into place temporarily, such as in one or more mobile data collectors, sensors that can be put into place for longer term use, such as being attached to points of interest on devices or systems, and the like.
In embodiments, a third party (e.g. RMOs, manufacturers) can aggregate data at the component level, equipment level, factory/installation level and provide a statistically valid data set against which to optimize their own systems. For example, when a new installation of a machine is contemplated, it may be beneficial to review a library for best data points to acquire in making state predictions. For example, a particular sensor package may be recommended to reliably determine if there will be a failure. For example, if vibration noise of equipment coupled with particular levels of local noise or other ambient sensed conditions reliably is an indicator of imminent failure, a given vibration transducer/temp/microphone package observing those elements may be recommended for the installation. Knowing such information may inform the choice to rent or buy a piece of machinery or associated warranties and service plans, such as based on knowing the quantity and depth of information that may be needed to reliably maintain the machinery.
In embodiments, manufacturers may utilize the library to rapidly collect in-service information for machines to draft engineering specifications for new customers.
In embodiments, noise and vibration data may be used to remotely monitor installs and automatically dispatch field crew.
In embodiments, noise and vibration data may be used to audit a system. For example, equipment running outside the range of a licensed duty cycle may be detected by a suite of vibration sensors and/or ambient/local noise sensors. In embodiments, alerts may be triggered of potential out-of-warranty violations based on data from vibration sensors and/or ambient/local noise sensors.
In embodiments, noise and vibration data may be used in maintenance. This may be particularly useful where multiple machines are deployed that may vibrationally interact with the environment, such as two large generating machines on the same floor or platform with each other, such as in power generation plants.
In embodiments, and as depicted in
In embodiments, a monitoring system 10800 for data collection in an industrial environment may include a data collection circuit 10808 structured to collect output data 10810 from a plurality of sensors 10802 selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors 10802 communicatively coupled to a data collection circuit 10808, and a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of at least one of an outcome and a state, wherein the monitoring system 10800 is structured to determine if the output data matches a learned received output data pattern. The machine learning data analysis circuit 10812 may be structured to learn received output data patterns 10814 by being seeded with a model 10816. The model 10816 may be a physical model, an operational model, or a system model. The machine learning data analysis circuit 10812 may be structured to learn received output data patterns 10814 based on the outcome or the state. The monitoring system 10800 keeps or modifies operational parameters or equipment based on the predicted outcome or the state. The data collection circuit 10808 collects more/fewer data points from one or more of the plurality of sensors 10802 based on the learned received output data patterns 10814, the outcome or the state. The data collection circuit 10808 changes a data storage technique for the output data based on the learned received output data patterns 10814, the outcome, or the state. The data collector 10804 changes a data presentation mode or manner based on the learned received output data patterns 10814, the outcome, or the state. The data collection circuit 10808 applies one or more filters (low pass, high pass, band pass, etc.) to the output data. The data collection circuit 10808 adjusts the weights/biases of the machine learning data analysis circuit 10812, such as in response to the learned received output data patterns 10814. The monitoring system 10800 removes/re-tasks under-utilized equipment based on one or more of the learned received output data patterns 10814, the outcome, or the state. The machine learning data analysis circuit 10812 may include a neural network expert system. The machine learning data analysis circuit 10812 may be structured to learn received output data patterns 10814 indicative of progress/alignment with one or more goals/guidelines, wherein progress/alignment of each goal/guideline is determined by a different subset of the plurality of sensors 10802. The machine learning data analysis circuit 10812 may be structured to learn received output data patterns 10814 indicative of an unknown variable. The machine learning data analysis circuit 10812 may be structured to learn received output data patterns 10814 indicative of a preferred input sensor among available input sensors. The machine learning data analysis circuit 10812 may be disposed in part on a machine, on one or more data collection circuit 10808s, in network infrastructure, in the cloud, or any combination thereof. The output data 10810 from the vibration sensors forms a vibration fingerprint, which may include one or more of a frequency, a spectrum, a velocity, a peak location, a wave peak shape, a waveform shape, a wave envelope shape, an acceleration, a phase information, and a phase shift. The data collection circuit 10808 may apply a rule regarding how many parameters of the vibration fingerprint to match or the standard deviation for the match in order to identify a match between the output data 10810 and the learned received output data pattern. The state may be one of a normal operation, a maintenance required, a failure, or an imminent failure. The monitoring system 10800 may trigger an alert, shuts down equipment/component/line, initiate maintenance/lubrication/alignment based on the predicted outcome or state, deploy a field technician based on the predicted outcome or state, recommend a vibration absorption/dampening device based on the predicted outcome or state, modify a process to utilize backup equipment/component based on the predicted outcome or state, and the like. The monitoring system 10800 may modify a process to preserve products/reactants, etc. based on the predicted outcome or state. The monitoring system 10800 may generate or modify a maintenance schedule based on the predicted outcome or state. The data collection circuit 10808 may include the data collection circuit 10808. The system may be deployed on the data collection circuit 10808 or distributed between the data collection circuit 10808 and a remote infrastructure.
In embodiments, a monitoring system 10800 for data collection in an industrial environment may include a data collection circuit 10808 structured to collect output data 10810 from a plurality of sensors 10802 selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors 10802 communicatively coupled to the data collection circuit 10808, and a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of at least one of an outcome and a state, wherein the monitoring system 10800 is structured to determine if the output data matches a learned received output data pattern and keep or modify operational parameters or equipment based on the determination
In embodiments, a monitoring system 10800 for data collection in an industrial environment may include a data collection circuit 10808 structured to collect output data 10810 from the plurality of sensors 10802 selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors 10802 communicatively coupled to the data collection circuit 10808, and a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of at least one of an outcome and a state, wherein the output data 10810 from the vibration sensors forms a vibration fingerprint. The vibration fingerprint may include one or more of a frequency, a spectra, a velocity, a peak location, a wave peak shape, a waveform shape, a wave envelope shape, an acceleration, a phase information, and a phase shift. The data collection circuit 10808 may apply a rule regarding how many parameters of the vibration fingerprint to match or the standard deviation for the match in order to identify a match between the output data 10810 and the learned received output data pattern. The monitoring system 10800 may be structured to determine if the output data matches a learned received output data pattern and keep or modify operational parameters or equipment based on the determination
In embodiments, a monitoring system 10800 for data collection in an industrial environment may include a data collection band circuit 10818 that identifies a subset of the plurality of sensors 10802 from which to process output data, the sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors 10802 communicatively coupled to a data collection band circuit 10818, a data collection circuit 10808 structured to collect the output data 10810 from the subset of plurality of sensors 10802, and a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of at least one of an outcome and a state, wherein when the learned received output data patterns 10814 do not reliably predict the outcome or the state, the data collection band circuit 10818 alters at least one parameter of at least one of the plurality of sensors 10802. A controller 10806 identifies a new data collection band circuit 10818 based on one or more of the learned received output data patterns 10814 and the outcome or state. The machine learning data analysis circuit 10812 may be further structured to learn received output data patterns 10814 indicative of a preferred input data collection band among available input data collection bands. The system may be deployed on the data collection circuit 10808 or distributed between the data collection circuit 10808 and a remote infrastructure.
In embodiments, a monitoring system for data collection in an industrial environment may include a data collection circuit 10808 structured to collect output data 10810 from a plurality of sensors 10802, the sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors 10802 communicatively coupled to the data collection circuit 10808, wherein the output data 10810 from the vibration sensors is in the form of a vibration fingerprint, a data structure 10820 comprising a plurality of vibration fingerprints and associated outcomes, and a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of an outcome or a state based on processing of the vibration fingerprints. The machine learning data analysis circuit 10812 may be seeded with one of the plurality of vibration fingerprints from the data structure 10820. The data structure 10820 may be updated if a changed parameter resulted in a new vibration fingerprint or if a predicted outcome did not occur in the absence of mitigation. The data structure 10820 may be updated when the learned received output data patterns 10814 do not reliably predict the outcome or the state. The system may be deployed on the data collection circuit or distributed between the data collection circuit and a remote infrastructure.
In embodiments, a monitoring system 10800 for data collection in an industrial environment may include a data collection circuit 10808 structured to collect output data 10810 from a plurality of sensors 10802 selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors 10802 communicatively coupled to a data collection circuit 10808, wherein the output data 10810 from the plurality of sensors 10802 is in the form of a noise pattern, a data structure 10820 comprising a plurality of noise patterns and associated outcomes, and a machine learning data analysis circuit 10812 structured to receive the output data 10810 and learn received output data patterns 10814 predictive of an outcome or a state based on processing of the noise patterns.
1. A monitoring system for data collection in an industrial environment, comprising:
a plurality of sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors communicatively coupled to a data collector;
a data collection circuit structured to collect output data from the plurality of sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of at least one of an outcome and a state.
2. The system of clause 1, wherein the state corresponds to an outcome relating to a machine in the environment.
3. The system of clause 1, wherein the state corresponds to an anticipated outcome relating to a machine in the environment.
4. The system of clause 1, wherein the state corresponds to an outcome relating to a process in the environment.
5. The system of clause 1, wherein the state corresponds to an anticipated outcome relating to a process in the environment.
6. The system of clause 1, wherein the system is deployed on the data collector.
7. The system of clause 1, wherein the system is distributed between the data collector and a remote infrastructure.
8. The system of clause 1, wherein the data collector comprises the data collection circuit.
9. The system of clause 1, wherein the ambient environment condition sensors include a noise sensor.
10. The system of clause 1, wherein the ambient environment condition sensors include a temperature sensor.
11. The system of clause 1, wherein the ambient environment condition sensors include a flow sensor.
12. The system of clause 1, wherein the ambient environment condition sensors include a pressure sensor.
13. The system of clause 1, wherein the ambient environment condition sensors include a chemical sensor.
14. The system of clause 1, wherein the local sensors include a noise sensor.
15. The system of clause 1, wherein the local sensors include a temperature sensor.
16. The system of clause 1, wherein the local sensors include a flow sensor.
17. The system of clause 1, wherein the local sensors include a pressure sensor.
18. The system of clause 1, wherein the local condition sensors include a chemical sensor.
19. The system of clause 1, wherein the ambient environment condition sensors comprise one or more of a vibration sensor, an acceleration sensor, an accelerometer, a Pressure sensor, a force sensor, a position sensor, a location sensor, a velocity sensor, a displacement sensor, a temperature sensor, a thermographic sensor, a heat flux sensor, a tachometer sensor, a motion sensor, a magnetic field sensor, an electrical field sensor, a galvanic sensor, a current sensor, a flow sensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, a heat flow sensor, a particulate flow sensor, a level sensor, a proximity sensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pH sensor, a hygrometer, a moisture sensor, a densitometer, an imaging sensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touch sensor, a microphone, a capacitive sensor, a strain gauge, and an EMI′ meter.
20. The system of clause 1, wherein the local sensors comprise one or more of a vibration sensor, an acceleration sensor, an accelerometer, a Pressure sensor, a force sensor, a position sensor, a location sensor, a velocity sensor, a displacement sensor, a temperature sensor, a thermographic sensor, a heat flux sensor, a tachometer sensor, a motion sensor, a magnetic field sensor, an electrical field sensor, a galvanic sensor, a current sensor, a flow sensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, a heat flow sensor, a particulate flow sensor, a level sensor, a proximity sensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pH sensor, a hygrometer, a moisture sensor, a densitometer, an imaging sensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touch sensor, a microphone, a capacitive sensor, a strain gauge, and an EMI′ meter.
21. A monitoring system for data collection in an industrial environment, comprising:
a data collection circuit structured to collect output data from a plurality of sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors communicatively coupled to the data collection circuit; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of at least one of an outcome and a state,
wherein the monitoring system is structured to determine if the output data matches a learned received output data pattern.
22. The system of clause 21, wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model.
23. The system of clause 22, wherein the model is a physical model, an operational model, or a system model.
24. The system of clause 21, wherein the machine learning data analysis circuit is structured to learn received output data patterns based on the outcome or the state.
25. The system of clause 21, wherein the monitoring system keeps or modifies operational parameters or equipment based on the predicted outcome or the state.
26. The system of clause 21, wherein the data collection circuit collects more/fewer data points from one or more of the plurality of sensors based on the learned received output data patterns, the outcome or the state.
27. The system of clause 21, wherein the data collection circuit changes a data storage technique for the output data based on the learned received output data patterns, the outcome, or the state.
28. The system of clause 21, wherein the data collection circuit changes a data presentation mode or manner based on the learned received output data patterns, the outcome, or the state.
29. The system of clause 21, wherein the data collection circuit applies one or more filters (low pass, high pass, band pass, etc.) to the output data
30. The system of clause 21, wherein the data collection circuit adjusts the weights/biases of the machine learning data analysis circuit.
31. The system of clause 30, wherein the adjustment is in response to the learned received output data patterns.
32. The system of clause 21, wherein the monitoring system removes/re-tasks under-utilized equipment based on one or more of the learned received output data patterns, the outcome, or the state.
33. The system of clause 21, wherein the machine learning data analysis circuit comprises a neural network expert system.
34. The system of clause 21, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of progress/alignment with one or more goals/guidelines.
35. The system of clause 34, wherein progress/alignment of each goal/guideline is determined by a different subset of the plurality of sensors.
36. The system of clause 21, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of an unknown variable.
37. The system of clause 21, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of a preferred input sensor among available input sensors.
38. The system of clause 21, wherein the machine learning data analysis circuit is disposed in part on a machine, on one or more data collectors, in network infrastructure, in the cloud, or any combination thereof.
39. The system of clause 21, wherein the output data from the vibration sensors forms a vibration fingerprint.
40. The system of clause 39, wherein the vibration fingerprint comprises one or more of a frequency, a spectra, a velocity, a peak location, a wave peak shape, a waveform shape, a wave envelope shape, an acceleration, a phase information, and a phase shift.
41. The system of clause 39, wherein the data collection circuit applies a rule regarding how many parameters of the vibration fingerprint to match or the standard deviation for the match in order to identify a match between the output data and the learned received output data pattern.
42. The system of clause 21, wherein the state is one of a normal operation, a maintenance required, a failure, or an imminent failure.
43. The system of clause 21, wherein the monitoring system triggers an alert based on the predicted outcome or state.
44. The system of clause 21, wherein the monitoring system shuts down equipment/component/line based on the predicted outcome or state.
45. The system of clause 21, wherein the monitoring system initiates maintenance/lubrication/alignment based on the predicted outcome or state.
46. The system of clause 21, wherein the monitoring system deploys a field technician based on the predicted outcome or state.
47. The system of clause 21, wherein the monitoring system recommends a vibration absorption/dampening device based on the predicted outcome or state.
48. The system of clause 21, wherein the monitoring system modifies a process to utilize backup equipment/component based on the predicted outcome or state.
49. The system of clause 21, wherein the monitoring system modifies a process to preserve products/reactants, etc. based on the predicted outcome or state.
50. The system of clause 21, wherein the monitoring system generates or modifies a maintenance schedule based on the predicted outcome or state.
51. The system of clause 21, wherein the data collection circuit comprises the data collection circuit
52. The system of clause 21, wherein the system is deployed on the data collector.
53. The system of clause 21, wherein the system is distributed between the data collector and a remote infrastructure.
54. A monitoring system for data collection in an industrial environment, comprising:
a data collection circuit structured to collect output data from a plurality of sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors communicatively coupled to the data collection circuit; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of at least one of an outcome and a state,
wherein the monitoring system is structured to determine if the output data matches a learned received output data pattern and keep or modify operational parameters or equipment based on the determination
55. A monitoring system for data collection in an industrial environment, comprising:
a data collection circuit structured to collect output data from a plurality of sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors communicatively coupled to the data collection circuit; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of at least one of an outcome and a state, wherein the output data from the vibration sensors forms a vibration fingerprint.
56. The system of clause 55, wherein the vibration fingerprint comprises one or more of a frequency, a spectra, a velocity, a peak location, a wave peak shape, a waveform shape, a wave envelope shape, an acceleration, a phase information, and a phase shift.
57. The system of clause 56, wherein the data collection circuit applies a rule regarding how many parameters of the vibration fingerprint to match or the standard deviation for the match in order to identify a match between the output data and the learned received output data pattern.
58. The system of clause 55, wherein the monitoring system is structured to determine if the output data matches a learned received output data pattern and keep or modify operational parameters or equipment based on the determination.
59. A monitoring system for data collection in an industrial environment, comprising:
a data collection band circuit that identifies a subset of a plurality of sensors from which to process output data, the sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors communicatively coupled to the data collection band circuit;
a data collection circuit structured to collect the output data from the subset of plurality of sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of at least one of an outcome and a state;
wherein when the learned received output data patterns do not reliably predict the outcome or the state, the data collection band circuit alters at least one parameter of at least one of the plurality of sensors.
60. The system of clause 59, wherein the controller identifies a new data collection band circuit based on one or more of the learned received output data patterns and the outcome or state.
61. The system of clause 59, wherein the machine learning data analysis circuit is further structured to learn received output data patterns indicative of a preferred input data collection band among available input data collection bands
62. The system of clause 59, wherein the system is deployed on the data collection circuit.
63. The system of clause 59, wherein the system is distributed between the data collection circuit and a remote infrastructure.
64. A monitoring system for data collection in an industrial environment, comprising:
a data collection circuit structured to collect output data from the plurality of sensors, the sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment and being communicatively coupled to the data collection circuit, wherein the output data from the vibration sensors is in the form of a vibration fingerprint;
a data structure comprising a plurality of vibration fingerprints and associated outcomes; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of an outcome or a state based on processing of the vibration fingerprints.
65. The system of clause 64, wherein the machine learning data analysis circuit is seeded with one of the plurality of vibration fingerprints from the data structure.
66. The system of clause 64, wherein the data structure is updated if a changed parameter resulted in a new vibration fingerprint or if a predicted outcome did not occur in the absence of mitigation.
67. The system of clause 64, wherein the data structure is updated when the learned received output data patterns do not reliably predict the outcome or the state.
68. The system of clause 64, wherein the system is deployed on the data collection circuit.
69. The system of clause 64, wherein the system is distributed between the data collection circuit and a remote infrastructure.
70. A monitoring system for data collection in an industrial environment, comprising:
a data collection circuit structured to collect output data from the plurality of sensors selected among vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment, the plurality of sensors communicatively coupled to the data collection circuit, wherein the output data from the plurality of sensors is in the form of a noise pattern;
a data structure comprising a plurality of noise patterns and associated outcomes; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns predictive of an outcome or a state based on processing of the noise patterns.
An example system for data collection in an industrial environment includes an industrial system having a number of components, and a number of sensors wherein each of the sensors is operatively coupled to at least one of the components. The example system further includes a sensor communication circuit that interprets a number of sensor data values in response to a sensed parameter group, a pattern recognition circuit that determines a recognized pattern value in response to a least a portion of the sensor data values, and a sensor learning circuit that updates the sensed parameter group in response to the recognized pattern value. The example sensor communication circuit further adjusts the interpreting the sensor data values in response to the updated sensed parameter group.
Certain further aspects of an example system are described following, any one or more of which may be present in certain embodiments. An example system includes the sensed parameter group being a fused number of sensors, and where the recognized pattern value further includes a secondary value including a value determined in response to the fused number of sensors. An example system further includes the pattern recognition circuit and the sensor learning circuit iteratively performing the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value. An example system further includes the sensing performance value include a determination of one or more of the following: a signal-to-noise performance for detecting a value of interest in the industrial system; a network utilization of the sensors in the industrial system; an effective sensing resolution for a value of interest in the industrial system; a power consumption value for a sensing system in the industrial system, the sensing system including the sensors; a calculation efficiency for determining the secondary value; an accuracy and/or a precision of the secondary value; a redundancy capacity for determining the secondary value; and/or a lead time value for determining the secondary value. Example and non-limiting calculation efficiency values include one or more determinations such as: processor operations to determine the secondary value; memory utilization for determining the secondary value; a number of sensor inputs from the number of sensors for determining the secondary value; and/or supporting data long-term storage for supporting the secondary value.
An example system includes one or more, or all, of the sensors as analog sensors and/or as remote sensors. An example system includes the secondary value being a value such as: a virtual sensor output value; a process prediction value; a process state value; a component prediction value; a component state value; and/or a model output value having the sensor data values from the fused number of sensors as an input. An example system includes the fused number of sensors being one or more of the combinations of sensors such as: a vibration sensor and a temperature sensor; a vibration sensor and a pressure sensor; a vibration sensor and an electric field sensor; a vibration sensor and a heat flux sensor; a vibration sensor and a galvanic sensor; and/or a vibration sensor and a magnetic sensor.
An example sensor learning circuit further updates the sensed parameter group by performing an operation such as: updating a sensor selection of the sensed parameter group; updating a sensor sampling rate of at least one sensor from the sensed parameter group; updating a sensor resolution of at least one sensor from the sensed parameter group; updating a storage value corresponding to at least one sensor from the sensed parameter group; updating a priority corresponding to at least one sensor from the sensed parameter group; and/or updating at least one of a sampling rate, sampling order, sampling phase, and/or a network path configuration corresponding to at least one sensor from the sensed parameter group. An example pattern recognition circuit further determines the recognized pattern value by performing an operation such as: determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest; determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; and/or updating the recognized pattern value in response to external feedback. Example and non-limiting values of interest include: a virtual sensor output value; a process prediction value; a process state value; a component prediction value; a component state value; and/or a model output value having the sensor data values from the fused plurality of sensors as an input.
An example pattern recognition circuit further accesses cloud-based data including a second number of sensor data values, the second number of sensor data values corresponding to at least one offset industrial system. An example sensor learning circuit further accesses the cloud-based data including a second updated sensor parameter group corresponding to the at least one offset industrial system.
An example procedure for data collection in an industrial environment includes an operation to provide a number of sensors to an industrial system including a number of components, each of the number of sensors operatively coupled to at least one of the number of components, an operation to interpret a number of sensor data values in response to a sensed parameter group, the sensed parameter group including a fused number of sensors from the number of sensors, an operation to determine a recognized pattern value including a secondary value determined in response to the number of sensor data values, an operation to update the sensed parameter group in response to the recognized pattern value, and an operation to adjust the interpreting the number of sensor data values in response to the updated sensed parameter group.
Certain further aspects of an example procedure are described following, any one or more of which may be included in certain embodiments. An example procedure includes an operation to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value; where determining the sensing performance value includes an least one operation for determining a value, such as determining: a signal-to-noise performance for detecting a value of interest in the industrial system; a network utilization of the plurality of sensors in the industrial system; an effective sensing resolution for a value of interest in the industrial system; a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors; a calculation efficiency for determining the secondary value; an accuracy and/or a precision of the secondary value; a redundancy capacity for determining the secondary value; and/or a lead time value for determining the secondary value.
An example procedure includes an operation to update the sensed parameter group comprises by performing at least one operation such as: updating a sensor selection of the sensed parameter group; updating a sensor sampling rate of at least one sensor from the sensed parameter group; updating a sensor resolution of at least one sensor from the sensed parameter group; updating a storage value corresponding to at least one sensor from the sensed parameter group; updating a priority corresponding to at least one sensor from the sensed parameter group; and/or updating at least one of a sampling rate, sampling order, sampling phase, and a network path configuration corresponding to at least one sensor from the sensed parameter group. An example procedure includes determining the recognized pattern value by performing at least one operation such as: determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest; determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; and/or updating the recognized pattern value in response to external feedback.
The term industrial system (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, an industrial system includes any large scale process system, mechanical system, chemical system, assembly line, oil and gas system (including, without limitation, production, transportation, exploration, remote operations, offshore operations, and/or refining), mining system (including, without limitation, production, exploration, transportation, remote operations, and/or underground operations), rail system (yards, trains, shipments, etc.), construction, power generation, aerospace, agriculture, food processing, and/or energy generation. Certain components may not be considered industrial individually, but may be considered industrially in an aggregated system—for example a single fan, motor, and/or engine may be not an industrial system, but may be a part of a larger system and/or be accumulated with a number of other similar components to be considered an industrial system and/or a part of an industrial system. In certain embodiments, a system may be considered an industrial system for some purposes but not for other purposes—for example a large data server farm may be considered an industrial system for certain sensing operations, such as temperature detection, vibration, or the like, but not an industrial system for other sensing operations such as gas composition. Additionally, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether such system are industrial systems, and/or which type of industrial system. For example, one data server farm may not, at a given time, have process stream flow rates that are critical to operation, while another data server farm may have process stream flow rates that are critical to operation (e.g., a coolant flow stream), and accordingly one data farm server may be an industrial system for a data collection and/or sensing improvement process or system, while the other is not. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered an industrial system herein, while in certain embodiments a given system may not be considered an industrial system herein. One of skill in the art, having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, how to combine processes and systems from the present disclosure to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is an industrial system and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: the accessibility of portions of the system to positioning sensing devices; the sensitivity of the system to capital costs (e.g., initial installation) and operating costs (e.g., optimization of processes, reduction of power usage); the transmission environment of the system (e.g., availability of broadband internet; satellite coverage; wireless cellular access; the electro-magnetic (EM) environment of the system; the weather, temperature, and environmental conditions of the system; the availability of suitable locations to run wires, network lines, and the like; the presence and/or availability of suitable locations for network infrastructure, router positioning, and/or wireless repeaters); the availability of trained personnel to interact with computing devices; the desired spatial, time, and/or frequency resolution of sensed parameters in the system; the degree to which a system or process is well understood or modeled; the turndown ratio in system operations (e.g., high load differential to low load; high flow differential to low flow; high temperature operation differential to low temperature operation); the turndown ratio in operating costs (e.g.; effects of personnel costs based on time (day, season, etc.); effects of power consumption cost variance with time, throughput, etc.); the sensitivity of the system to failure, down-time, or the like; the remoteness of the contemplated system (e.g., transport costs, time delays, etc.); and/or qualitative scope of change in the system over the operating cycle (e.g., the system runs several distinct processes requiring a variable sensing environment with time; time cycle and nature of changes such as periodic, event driven, lead times generally available, etc.). While specific examples of industrial systems and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.
The term sensor (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, sensor includes any device configured to provide a sensed value representative of a physical value (e.g., temperature, force, pressure) in a system, or representative of a conceptual value in a system at least having an ancillary relationship to a physical value (e.g., work, state of charge, frequency, phase, etc.).
Example and non-limiting sensors include vibration, acceleration, noise, pressure, force, position, location, velocity, displacement, temperature, heat flux, speed, rotational speed (e.g., a tachometer), motion, accelerometers, magnetic field, electrical field, galvanic, current, flow (gas, fluid, heat, particulates, particles, etc.), level, proximity, gas composition, fluid composition, toxicity, corrosiveness, acidity, pH, humidity, hygrometer, moisture, density (bulk or specific), ultrasound, imaging, analog, and/or digital sensors. The list of sensed values is a non-limiting example, and the benefits of the present disclosure in many applications can be realized independent of the sensor type, while in other applications the benefits of the present disclosure may be dependent upon the sensor type.
The sensor type and mechanism for detection may be any type of sensor understood in the art. Without limitation, an accelerometer may be any type and scaling, for example 500 mV per g (1 g=9.8 m/s2), 100 mV, 1 V per g, 5 V per g, 10 V per g, 10 MV per g, as well as any frequency capability. It will be understood for accelerometers, and for all sensor types, that the scaling and range may be competing (e.g., in a fixed-bit or low bit A/D system), and/or selection of high resolution scaling with a large range may drive up sensor and/or computing costs, which may be acceptable in certain embodiments, and may be prohibitive in other embodiments. Example and non-limiting accelerometers include piezo-electric devices, high resolution and sampling speed position detection devices (e.g., laser based devices), and/or detection of other parameters (strain, force, noise, etc.) that can be correlated to acceleration and/or vibration. Example and non-limiting proximity probes include electro-magnetic devices (e.g., Hall effect, Variable Reluctance, etc.), a sleeve/oil film device, and/or determination of other parameters than can be correlated to proximity. An example vibration sensor includes a tri-axial probe, which may have high frequency response (e.g., scaling of 100 MV/g). Example and non-limiting temperature sensors include thermistors, thermocouples, and/or optical temperature determination.
A sensor may, additionally or alternatively, provide a processed value (e.g., a de-bounced, filtered, and/or compensated value) and/or a raw value, with processing downstream (e.g., in a data collector, controller, plant computer, and/or on a cloud-based data receiver). In certain embodiments, a sensor provides a voltage, current, data file (e.g., for images), or other raw data output, and/or a sensor provides a value representative of the intended sensed measurement (e.g., a temperature sensor may communicate a voltage or a temperature value). Additionally or alternatively, a sensor may communicate wirelessly, through a wired connection, through an optical connection, or by any other mechanism. The described examples of sensor types and/or communication parameters are non-limiting examples for purposes of illustration.
Additionally or alternatively, in certain embodiments, a sensor is a distributed physical device—for example where two separate sensing elements coordinate to provide a sensed value (e.g., a position sensing element and a mass sensing element may coordinate to provide an acceleration value). In certain embodiments, a single physical device may form two or more sensors, and/or parts of more than one sensor. For example, a position sensing element may form a position sensor and a velocity sensor, where the same physical hardware provides the sensed data for both determinations.
The term smart sensor, smart device (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a smart sensor includes any sensor and aspect thereof as described throughout the present disclosure. A smart sensor includes an increment of processing reflected in the sensed value communicated by the sensor, including at least basic sensor processing (e.g., de-bouncing, filtering, compensation, normalization, and/or output limiting), more complex compensations (e.g., correcting a temperature value based on known effects of current environmental conditions on the sensed temperature value, common mode or other noise removal, etc.), a sensing device that provides the sensed value as a network communication, and/or a sensing device that aggregates a number of sensed values for communication (e.g., multiple sensors on a device communicated out in a parseable or deconvolutable manner or as separate messages; multiple sensors providing a value to a single smart sensor, which relays sensed values on to a data collector, controller, plant computer, and/or cloud-based data receiver). The use of the term smart sensor is for purposes of illustration, and whether a sensor is a smart sensor can depend upon the context and the contemplated system, and can be a relative description compared to other sensors in the contemplated system. Thus, a given sensor having identical functionality may be a smart sensor for the purposes of one contemplated system, and just a sensor for the purposes of another contemplated system, and/or may be a smart sensor in a contemplated system during certain operating conditions, and just a sensor for the purposes of the same contemplated system during other operating conditions.
The terms sensor fusion, fused sensors, and similar terms, as utilized herein, should be understood broadly, except where context indicates otherwise, without limitation to any other aspect or description of the present disclosure, a sensor fusion includes a determination of second order data from sensor data, and further includes a determination of second order data from sensor data of multiple sensors, including involving multiplexing of streams of data, combinations of batches of data, and the like from the multiple sensors. Second order data includes a determination about a system or operating condition beyond that which is sensed directly. For example, temperature, pressure, mixing rate, and other data may be analyzed to determine which parameters are result-effective on a desired outcome (e.g., a reaction rate). The sensor fusion may include sensor data from multiple sources, and/or longitudinal data (e.g., taken over a period of time, over the course of a process, and/or over an extent of components in a plant—for example tracking a number of assembled parts, a virtual slug of fluid passing through a pipeline, or the like). The sensor fusion may be performed in real-time (e.g., populating a number of sensor fusion determinations with sensor data as a process progresses), off-line (e.g., performed on a controller, plant computer, and/or cloud-based computing device), and/or as a post-processing operation (e.g., utilizing historical data, data from multiple plants or processes, etc.). In certain embodiments, a sensor fusion includes a machine pattern recognition operation—for example where an outcome of a process is given to the machine and/or determined by the machine, and the machine pattern recognition operation determines result-effective parameters from the detected sensor value space to determine which operating conditions were likely to be the cause of the outcome and/or the off-nominal result of the outcome (e.g., process was less effective or more effective than nominal, failed, etc.). In certain embodiments, the outcome may be a quantitative outcome (e.g., 20% more product was produced than a nominal run) or a qualitative outcome (e.g., product quality was unacceptable, component X of the contemplated system failed during the process, component X of the contemplated system required a maintenance or service event, etc.).
In certain embodiments, a sensor fusion operation is iterative or recursive—for example an estimated set of result effective parameters is updated after the sensor fusion operation, and a subsequent sensor fusion operation is performed on the same data or another data set with an updated set of the result effective parameters. In certain embodiments, subsequent sensor fusion operations include adjustments to the sensing scheme—for example higher resolution detections (e.g., in time, space, and/or frequency domains), larger data sets (and consequent commitment of computing and/or networking resources), changes in sensor capability and/or settings (e.g., changing an A/D scaling, range, resolution, etc.; changing to a more capable sensor and/or more capable data collector, etc.) are performed for subsequent sensor fusion operations. In certain embodiments, the sensor fusion operation demonstrates improvements to the contemplated system (e.g., production quantity, quality, and/or purity, etc.) such that expenditure of additional resources to improve the sensing scheme are justified. In certain embodiments, the sensor fusion operation provides for improvement in the sensing scheme without incremental cost—for example by narrowing the number of result effective parameters and thereby freeing up system resources to provide greater resolution, sampling rates, etc., from hardware already present in the contemplated system. In certain embodiments, iterative and/or recursive sensor fusion is performed on the same data set, a subsequent data set, and/or a historical data set. For example, high resolution data may already be present in the system, and a first sensor fusion operation is performed with low resolution data (e.g., sampled from the high resolution data set), such as to allow for completion of sensor fusion processing operations within a desired time frame, within a desired processor, memory, and/or network utilization, and/or to allow for checking a large number of variables as potential result effective parameters. In a further example, a greater number of samples from the high resolution data set may be utilized in a subsequent sensor fusion operation in response to confidence that improvements are present, narrowing of the potential result effective variables, and/or a determination that higher resolution data is required to determine the result effective parameters and/or effective values for such parameters.
The described operations and aspects for sensor fusion are non-limiting examples, and one of skill in the art, having the benefit of the disclosures herein and information ordinarily available about a contemplated system, can readily design a system to utilize and/or benefit from a sensor fusion operation. Certain considerations for a system to utilize and/or benefit from a sensor fusion operation include, without limitation: the number of components in the system; the cost of components in the system; the cost of maintenance and/or down-time for the system; the value of improvements in the system (production quantity, quality, yield, etc.); the presence, possibility, and/or consequences of undesirable system outcomes (e.g., side products, thermal and/or luminary events, environmental benefits or consequences, hazards present in the system); the expense of providing a multiplicity of sensors for the system; the complexity between system inputs and system outputs; the availability and cost of computing resources (e.g., processing, memory, and/or communication throughput); the size/scale of the contemplated system and/or the ability of such a system to generate statistically significant data; whether offset systems exist, including whether data from offset systems is available and whether combining data from offset systems will generate a statistically improved data set relative to the system considered alone; and/or the cost of upgrading, improving, or changing a sensing scheme for the contemplated system. The described considerations for a contemplated system that may benefit from or utilize a sensor fusion operation are non-limiting illustrations.
Certain systems, processes, operations, and/or components are described in the present disclosure as “offset systems” or the like. An offset system is a system distinct from a contemplated system, but having relevance to the contemplated system. For example, a contemplated refinery may have an “offset refinery”, which may be a refinery operated by a competitor, by a same entity operating the contemplated refinery, and/or a historically operated refinery that no longer exists. The offset refinery bears some relevant relationship to the contemplated refinery, such as utilizing similar reactions, process flows, production volumes, feed stock, effluent materials, or the like. A system which is an offset system for one purpose may not be an offset system for another purpose. For example, a manufacturing process utilizing conveyor belts and similar motors may be an offset process for a contemplated manufacturing process for the purpose of tracking product movement, understanding motor operations and failure modes, or the like, but may not be an offset process for product quality if the products being produced have distinct quality outcome parameters. Any industrial system contemplated herein may have an offset system for certain purposes. One of skill in the art, having the benefit of the present disclosure and information ordinarily available for a contemplated system, can readily determine what is disclosed by an offset system or offset aspect of a system.
Any one or more of the terms computer, computing device, processor, circuit, and/or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and/or server. Additionally or alternatively, a computer, computing device, processor, circuit, and/or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and/or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and/or output devices, network and/or communication resources, memory resources of any type, processing resources of any type, and/or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein.
Certain operations described herein include interpreting, receiving, and/or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and/or determining any value parameter, input, data, and/or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and/or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value. For example, when communications are down, intermittent, or interrupted, a first operation to interpret, receive, and/or determine a data value may be performed, and when communications are restored an updated operation to interpret, receive, and/or determine the data value may be performed.
Certain logical groupings of operations herein, for example methods or procedures of the current disclosure, are provided to illustrate aspects of the present disclosure. Operations described herein are schematically described and/or depicted, and operations may be combined, divided, re-ordered, added, or removed in a manner consistent with the disclosure herein. It is understood that the context of an operational description may require an ordering for one or more operations, and/or an order for one or more operations may be explicitly disclosed, but the order of operations should be understood broadly, where any equivalent grouping of operations to provide an equivalent outcome of operations is specifically contemplated herein. For example, if a value is used in one operational step, the determining of the value may be required before that operational step in certain contexts (e.g. where the time delay of data for an operation to achieve a certain effect is important), but may not be required before that operation step in other contexts (e.g. where usage of the value from a previous execution cycle of the operations would be sufficient for those purposes). Accordingly, in certain embodiments an order of operations and grouping of operations as described is explicitly contemplated herein, and in certain embodiments re-ordering, subdivision, and/or different grouping of operations is explicitly contemplated herein.
Referencing
The example system 10902 further includes a sensor communication circuit 10920 (reference
In certain embodiments, sensor data values 10948 are provided to a data collector 10910, which may be in communication with multiple sensors 10908 and/or with a controller 10914. In certain embodiments, a plant computer 10912 is additionally or alternatively present. In the example system, the controller 10914 is structured to functionally execute operations of the sensor communication circuit 10920, pattern recognition circuit 10922, and/or the sensor learning circuit 10924, and is depicted as a separate device for clarity of description. Aspects of the controller 10914 may be present on the sensors 10908, the data collector 10910, the plant computer 10912, and/or on a cloud computing device 10916. In certain embodiments, all aspects of the controller 10914 may be present in another device depicted on the system 10902. The plant computer 10912 represents local computing resources, for example processing, memory, and/or network resources, that may be present and/or in communication with the industrial system 10902. In certain embodiments, the cloud computing device 10916 represents computing resources externally available to the industrial system 10902, for example over a private network, intra-net, through cellular communications, satellite communications, and/or over the internet. In certain embodiments, the data collector 10910 may be a computing device, a smart sensor, a MUX box, or other data collection device capable to receive data from multiple sensors and to pass-through the data and/or store data for later transmission. An example data collector 10910 has no storage and/or limited storage, and selectively passes sensor data therethrough, with a subset of the sensor data being communicated at a given time due to bandwidth considerations of the data collector 10910, a related network, and/or imposed by environmental constraints. In certain embodiments, one or more sensors and/or computing devices in the system 10902 are portable devices—for example a plant operator walking through the industrial system may have a smart phone, which the system 10902 may selectively utilize as a data collector 10910, sensor 10908—for example to enhance communication throughput, sensor resolution, and/or as a primary method for communicating sensor data values 10948 to the controller 10914.
The example system 10902 further includes a pattern recognition circuit 10922 that determines a recognized pattern value 10930 in response to a least a portion of the sensor data values 10948.
The example system 10902 further includes a sensor learning circuit 10924 that updates the sensed parameter group 10928 in response to the recognized pattern value 10930. The example sensor communication circuit 10920 further adjusts the interpreting the sensor data values 10948 in response to the updated sensed parameter group 10928.
An example system 10902 further includes the pattern recognition circuit 10922 and the sensor learning circuit 10924 iteratively performing the determining the recognized pattern value 10930 and the updating the sensed parameter group 10928 to improve a sensing performance value 10934. For example, the pattern recognition circuit 10922 may add sensors, remove sensors, and/or change sensor setting to modify the sensed parameter group 10928 based upon sensors which appear to be effective or ineffective predictors of the recognized pattern value 10930, and the sensor learning circuit 10924 may instruct a continued change (e.g., while improvement is still occurring), an increased or decreased rate of change (e.g., to converge more quickly on an improved sensed parameter group 10928), and/or instruct a randomized change to the sensed parameter group 10928 (e.g., to ensure that all potentially result effective sensors are being checked, and/or to avoid converging into a local optimal value).
Example and non-limiting options for the sensing performance value 10934 include: a signal-to-noise performance for detecting a value of interest in the industrial system (e.g., a determination that the prediction signal for the value is high relative to noise factors for one or more sensors of the sensed parameter group 10928, and/or for the sensed parameter group 10928 as a whole); a network utilization of the sensors in the industrial system (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it is as effective or almost as effective as another sensed parameter group 10928, but results in lower network utilization); an effective sensing resolution for a value of interest in the industrial system (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it provides a responsive prediction of the output value to smaller changes in input values); a power consumption value for a sensing system in the industrial system, the sensing system including the sensors (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it is as effective or almost as effective as another sensed parameter group 10928, but results in lower power consumption); a calculation efficiency for determining the secondary value (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it is as effective or almost as effective as another sensed parameter group 10928 in determining the secondary value 10932, but results in fewer processor cycles, lower network utilization, and/or lower memory utilization including stored memory requirements as well as intermediate memory utilization such as buffers); an accuracy and/or a precision of the secondary value (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it provides a highly accurate and/or highly precise determination of the secondary value 10932); a redundancy capacity for determining the secondary value (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it provides similar capability and/or resource utilization, but provides for additional sensing redundancy, such as being more robust to gaps in data from one or more of the sensors in the sensed parameter group 10928); and/or a lead time value for determining the secondary value 10932 (e.g., the sensor learning circuit 10924 may score a sensed parameter group 10928 relatively high where it provides an improved or sufficient lead time in the secondary value 10932 determination—for example to assist in avoiding over-temperature operation, spoiling an entire production run, determining whether a component has sufficient service life to complete a production run, etc.). Example and non-limiting calculation efficiency values include one or more determinations such as: processor operations to determine the secondary value 10932; memory utilization for determining the secondary value 10932; a number of sensor inputs from the number of sensors for determining the secondary value 10932; and/or supporting memory, such as long-term storage or buffers for supporting the secondary value 10932.
An example system includes one or more, or all, of the sensors 10908 as analog sensors and/or as remote sensors. An example system includes the secondary value 10932 being a value such as: a virtual sensor output value;
a process prediction value (e.g., a success value for a production run, an overtemperature value, an overpressure value, a product quality value, etc.); a process state value (e.g., a stage of the process, a temperature at a time and location in the process); a component prediction value (e.g., a component failure prediction, a component maintenance or service prediction, a component response to an operating change prediction); a component state value (a remaining service life or maintenance interval for a component); and/or a model output value having the sensor data values 10948 from the fused number of sensors 10926 as an input. An example system includes the fused number of sensors 10926 being one or more of the combinations of sensors such as: a vibration sensor and a temperature sensor; a vibration sensor and a pressure sensor; a vibration sensor and an electric field sensor; a vibration sensor and a heat flux sensor; a vibration sensor and a galvanic sensor; and/or a vibration sensor and a magnetic sensor.
An example sensor learning circuit 10924 further updates the sensed parameter group 10928 by performing an operation such as: updating a sensor selection of the sensed parameter group 10928 (e.g., which sensors are sampled); updating a sensor sampling rate of at least one sensor from the sensed parameter group (e.g., how fast the sensors provide information, and/or how fast information is passed through the network); updating a sensor resolution of at least one sensor from the sensed parameter group (e.g., changing or requesting a change in a sensor resolution, utilizing additional sensors to provide greater effective resolution); updating a storage value corresponding to at least one sensor from the sensed parameter group (e.g., storing data from the sensor at a higher or lower resolution, and/or over a longer or shorter time period); updating a priority corresponding to at least one sensor from the sensed parameter group (e.g., moving a sensor up to a higher priority—for example if environmental conditions prevent data receipt from all planned sensors, and/or reducing a time lag between creation of the sensed data and receipt at the sensor learning circuit 10924); and/or updating at least one of a sampling rate, sampling order, sampling phase, and/or a network path configuration corresponding to at least one sensor from the sensed parameter group.
An example pattern recognition circuit 10922 further determines the recognized pattern value 10930 by performing an operation such as: determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest 10950 (e.g., determining that a sensor value is a good predictor of the value of interest 10950); determining a sensitivity of at least one sensor of the sensed parameter group 10928 and the updated sensed parameter group 10928 relative to the value of interest 10950 (e.g., determining the relative sensitivity of the determined value of interest to small changes in operating conditions based on the selected sensed parameter group 10928); determining a predictive confidence of at least one sensor of the sensed parameter group 10928 and the updated sensed parameter group 10928 relative to the value of interest 10950; determining a predictive delay time of at least one sensor of the sensed parameter group 10928 and the updated sensed parameter group 10928 relative to the value of interest 10950; determining a predictive accuracy of at least one sensor of the sensed parameter group 10928 and the updated sensed parameter group 10928 relative to the value of interest 10950; determining a classification precision of at least one sensor of the sensed parameter group 10928 (e.g., determining the accuracy of classification of a pattern by a machine classifier based on use of the at least one sensor); determining a predictive precision of at least one sensor of the sensed parameter group 10928 and the updated sensed parameter group 10928 relative to the value of interest 10950; and/or updating the recognized pattern value 10930 in response to external feedback, which may be received as external data 10952 (e.g., where an outcome is known, such as a maintenance event, product quality determination, production outcome determination, etc., the detection of the recognized pattern value 10930 is thereby improved according to the conditions of the system before the known outcome occurred). Example and non-limiting values of interest 10950 include: a virtual sensor output value; a process prediction value; a process state value; a component prediction value; a component state value; and/or a model output value having the sensor data values from the fused plurality of sensors as an input.
An example pattern recognition circuit 10922 further accesses cloud-based data 10954 including a second number of sensor data values, the second number of sensor data values corresponding to at least one offset industrial system. An example sensor learning circuit 10924 further accesses the cloud-based data 10954 including a second updated sensor parameter group corresponding to the at least one offset industrial system. Accordingly, the pattern recognition circuit 10922 can improve pattern recognition in the system based on increased statistical data available from an offset system. Additionally or alternatively, the sensor learning circuit 10924 can improve more rapidly and with greater confidence based upon the data from the offset system—including determining which sensors in the offset system were found to be effective an predicting system outcomes.
An example system includes an industrial system including an oil refinery. An example oil refinery includes one or more compressors for transferring fluids throughout the plant, and/or for pressurizing fluid streams (e.g., for reflux in a distillation column). Additionally or alternatively, the example oil refinery includes vacuum distillation, for example to fractionate hydrocarbons. The example oil refinery additionally includes various pipelines in the system for transferring fluids, bringing in feedstock, final product delivery, and the like. An example system includes a number of sensors configured to determine each aspect of a distillation column—for example temperatures of various fluid streams, temperatures and compositions of individual contact trays in the column, measurements of the feed and reflux, as well as of the effluent or separated products. The design of a distillation column is complex, and optimal design can depend upon the sizing of boilers, compressors, the contact conditions within the column, as well as the composition of feedstock which can vary significantly. Additionally, the optimal position for effective sensing of conditions in a pipeline can vary with fluid flow rates, environmental conditions (e.g., causing variation in heat transfer rates), the feedstock utilized, and other factors. Additionally, wear or loss of capability in a boiler, compressor, or other operating equipment can change the system response and capabilities, rendering a single point optimization, including where sensors should be positioned and how they should sample data, to be non-optimal as the system ages.
Provision of multiple sensors throughout the system can be costly, not necessarily because the sensors are expensive, but because they sensors provide data which may be prohibitive to transmit, store, and utilize. Cost may involve costs of transmitting over networks, as well as costs of operations, such as numbers of input/output operations (and time required to undertake such operations). The example system includes providing a large number of sensors throughout the system, and determining which of the sensors are effective for control and optimization of the distillation process. Additionally, as the feedstock and/or environmental conditions change, the optimal sensor package for both optimization and control may change. The example system utilizes a pattern recognition circuit to determine which sensors, including sensor fusion operations (including selection of groups, selection of multiplexing and combination, and the like), are effective in controlling the desired parameters of the distillation, and in determining the optimal values for temperatures, flow rates, entry trays for feed and reflux, and/or reflux rates. Additionally, the sensor learning circuit is capable, over time and/or utilizing offset oil refineries, to rapidly converge on various sensor packages that are appropriate for a multiplicity of operating conditions. If an unexpected operating condition occurs—for example an off-nominal operation of a compressor, the sensor learning circuit is capable to migrate the system to the correct sensing and operating conditions for the unexpected operating condition. The ability to flexibly utilize a multiplicity of sensors allows for the system to be flexible to changing conditions without providing for excessive capability in transmission and storage of sensor data. Accordingly, operations of the distillation column are improved and can be optimized for a large number of operating conditions. Additionally, alerts for the distillation column, based upon recognition of patterns indicating off-nominal operation, can be readily prepared to adjust or shut down the process before significant product quality loss and/or hazardous conditions develop. Example sensor fusion operations for a refinery include vibration information combined with temperatures, pressures, and/or composition (e.g., to determine compressor performance); temperature and pressure, temperature and composition, and/or composition and pressure (e.g., to determine feedstock variance, contact tray performance, and/or a component failure).
An example refinery system includes storage tanks and/or boiler feed water. Example system determinations include a sensor fusion to determine a storage tank failure and/or off-nominal operation, such as through a temperature and pressure fusion, and/or a vibration determination with a non-vibration determination (e.g., detecting leaks, air in the system, and/or a feed pump issue). Certain further example system determinations include a sensor fusion to determine a boiler feed water failure, such as through a sensor fusion including flow rate, pressure, temperature, and/or vibration. Any one or more of these parameters can be utilized to determine a system leak, failure, wear of a feed pump, scaling, and/or to reduce pumping losses while maintaining system flow rates. Similarly, an example industrial system includes a power generation system having a condensate and/or make-up water system, where a sensor fusion provides for a sensed parameter group and prediction of failures, maintenance, and the like.
An example industrial system includes an irrigation system for a field or a system of fields. Irrigations systems are subject to significant variability in the system (e.g., inlet pressures and/or water levels, component wear and maintenance) as well as environmental variability (e.g., types and distribution of crops planted, weather, soil moisture, humidity, seasonal variability in the sun, cloud coverage, and/or wind variance). Additionally, irrigation systems tend to be remotely located where high bandwidth network access, maintenance facilities, and/or even personnel for oversight are not readily available. An example system includes a multiplicity of sensors capable to detect conditions for the irrigation system, without requiring that all of the sensors transmit or store data on a continuous basis. The pattern recognition circuit can readily determine the most important set of sensors to effectively predict patterns and thus system conditions requiring a response (e.g., irrigation cycles, positioning, and the like). The sensor learning circuit provides for responsive migration of the sensed parameter group to variability, which may occur on slower (e.g., seasonal, climate change, etc.) or faster cycles (e.g., equipment failure, weather conditions, step change events such as planting or harvesting). Additionally, alerts for remote facilities can be readily prepared, with confidence that the correct sensor package is in place for determining an off-nominal condition (e.g., imminent failure or maintenance requirement for a pump).
An example industrial system includes a chemical or pharmaceutical plant. Chemical plants require specific operating conditions, flow rates, temperatures, and the like to maintain proper temperatures, concentrations, mixing, and the like throughout the system. In many systems, there are numerous process steps, and an off-nominal or uncoordinated operation in one part of the process can result in reduced yields, a failed process, and/or a significant reduction in production capacity as coordinated processes must respond (or as coordinated processes fail to respond). Accordingly, a very large number of systems are required to minimally define the system, and in certain embodiments a prohibitive number of sensors are required, from a data transmission and storage viewpoint, to keep sensing capability for a broad range of operating conditions. Additionally, the complexity of the system results in difficulty optimizing and coordinating system operations even where sufficient sensors are present. In certain embodiments, the pattern recognition circuit can determine the sensing parameter groups that provide high resolution understanding of the system, without requiring that all of the sensors store and transmit data continuously. Further, the utilization of a sensor fusion provides for the opportunity to abstract desired outputs, for example “maximize yield” or “minimize an undesirable side reaction” without requiring a full understanding from the operator of which sensors and system conditions are most effective to achieve the abstracted desired output. Example components in a chemical or pharmaceutical plan amenable to control and predictions based on a sensor fusion operation include an agitator, a pressure reactor, a catalytic reactor, and/or a thermic heating system. Example sensor fusion operations to determine sensed parameter groups and tune the pattern recognition circuit include, without limitation, a vibration sensor combined with another sensor type, a composition sensor combined with another sensor type, a flow rate determination combined with another sensor type, and/or a temperature sensor combined with another sensor type. The sensor fusion best suited for a particular application can be converged upon by the sensor learning circuit, but also depends upon the type of component that is subject to predictions, as well as the type of desired outputs pursued by the operator. For example, agitators are amenable to vibration sensing, as well as uniformity of composition detection (e.g., high resolution temperature), expected reaction rates in a properly mixed system, and the like. Catalytic reactors are amenable to temperature sensing (based on the reaction thermodynamics), composition detection (e.g., for expected reactants, as well as direct detection of catalytic material), flow rates (e.g., gross mechanical failure, reduced volume of beads, etc.), and/or pressure detection (e.g., indicative of or coupled with flow rate changes).
An example industrial system includes a food processing system. Example food processing systems include pressurization vessels, stirrers, mixers, and/or thermic heating systems. Control of the process is critical to maintain food safety, product quality, and product consistency. However, most input parameters to the food processing system are subject to high variability—for example basic food products are inherently variable as natural products, with differing water content, protein content, and aesthetic variation. Additionally, labor cost management, power cost management, and variability in supply water, etc., provide for a complex process where determination of the process control variables, sensed parameters to determine these, and optimization of sensing in response to process variation are a difficult problem to resolve. Food processing systems are often cost conscious, and capital costs (e.g., for a robust network and computing system for optimization) are not readily incurred. Further, a food processing system may manufacture wide variance of products on similar or the same production facilities, for example to support an entire product line and/or due to seasonal variations, and accordingly a sensor setup for one process may not support another process well. An example system includes the pattern recognition circuit determining the sensing parameter groups that provide a strong signal response in target outcomes even in light of high variability in system conditions. The pattern recognition circuit can provide for numerous sensed group parameter options available for different process conditions without requiring extensive computing or data storage resources. Additionally, the sensor learning circuit provides for rapid response of the sensing system to changes in the process conditions, including updating the sensed group parameter options to pursue abstracted target outputs without the operator having to understand which sensed parameters best support the output goals. The sensor fusion best suited for a particular application can be converged upon by the sensor learning circuit, but also depends upon the type of component that is subject to predictions, as well as the type of desired outputs pursued by the operator. For example, control of and predictions for pressurization vessels, stirrers, mixers, and/or thermic heating systems are amenable to a sensor fusion with a temperature determination combined with a non-temperature determination, a vibration determination combined with a non-vibration determination, and/or a heat map combined with a rate of change in the heat map and/or a non-heat map determination. An example system includes a sensor fusion with a vibration determination and a non-vibration determination, wherein predictive information for a mixer and/or a stirrer is provided. An example system includes a sensor fusion with a pressure determination, a temperature determination, and/or a non-pressure determination, wherein predictive information for a pressurization vessel is provided.
Referencing
An example procedure 10936 includes an operation to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value (e.g., by repeating operations 10940 to 10944 periodically, at selected intervals, and/or in response to a system change). An example procedure 10936 includes determining the sensing performance value by determining: a signal-to-noise performance for detecting a value of interest in the industrial system; a network utilization of the plurality of sensors in the industrial system; an effective sensing resolution for a value of interest in the industrial system; a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors; a calculation efficiency for determining the secondary value; an accuracy and/or a precision of the secondary value; a redundancy capacity for determining the secondary value; and/or a lead time value for determining the secondary value.
An example procedure 10936 includes the operation 10944 to update the sensed parameter group by performing at least one operation such as: updating a sensor selection of the sensed parameter group; updating a sensor sampling rate of at least one sensor from the sensed parameter group; updating a sensor resolution of at least one sensor from the sensed parameter group; updating a storage value corresponding to at least one sensor from the sensed parameter group; updating a priority corresponding to at least one sensor from the sensed parameter group; and/or updating at least one of a sampling rate, sampling order, sampling phase, and a network path configuration corresponding to at least one sensor from the sensed parameter group. An example procedure 10936 includes the operation 10942 to determine the recognized pattern value by performing at least one operation such as: determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest; determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; and/or updating the recognized pattern value in response to external feedback.
1. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values; and
a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value;
wherein the sensor communication circuit is further structured to adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group.
2. The system of clause 1, wherein the sensed parameter group comprises a fused plurality of sensors, and wherein the recognized pattern value further includes a secondary value comprising a value determined in response to the fused plurality of sensors.
3. The system of clause 2, wherein the pattern recognition circuit and sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value.
4. The system of clause 3, wherein the sensing performance value comprises at least one performance determination selected from the performance determinations consisting of:
a signal-to-noise performance for detecting a value of interest in the industrial system;
a network utilization of the plurality of sensors in the industrial system;
an effective sensing resolution for a value of interest in the industrial system; and
a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.
5. The system of clause 3, wherein the sensing performance value comprises a signal-to-noise performance for detecting a value of interest in the industrial system.
6. The system of clause 3, wherein the sensing performance value comprises a network utilization of the plurality of sensors in the industrial system.
7. The system of clause 3, wherein the sensing performance value comprises an effective sensing resolution for a value of interest in the industrial system.
8. The system of clause 3, wherein the sensing performance value comprises a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.
9. The system of clause 3, wherein the sensing performance value comprises a calculation efficiency for determining the secondary value.
10 The system of clause 9, wherein the calculation efficiency comprises at least one of: processor operations to determine the secondary value, memory utilization for determining the secondary value, a number of sensor inputs from the plurality of sensors for determining the secondary value, and supporting data long-term storage for supporting the secondary value.
11. The system of clause 3, wherein the sensing performance value comprises one of an accuracy and a precision of the secondary value.
12. The system of clause 3, wherein the sensing performance value comprises a redundancy capacity for determining the secondary value.
13. The system of clause 3, wherein the sensing performance value comprises a lead time value for determining the secondary value.
14. The system of clause 13, wherein the secondary value comprises a component overtemperature value.
15. The system of clause 13, wherein the secondary value comprises one of a component maintenance time, a component failure time, and a component service life.
16. The system of clause 13, wherein the secondary value comprises an off nominal operating condition affecting a product quality produced by an operation of the industrial system.
17. The system of clause 1, wherein the plurality of sensors comprises at least one analog sensor.
18. The system of clause 1, wherein at least one of the sensors comprises a remote sensor.
19. The system of clause 2, wherein the secondary value comprises at least one value selected from the values consisting of:
a virtual sensor output value;
a process prediction value;
a process state value;
a component prediction value;
a component state value; and
a model output value having the sensor data values from the fused plurality of sensors as an input.
20. The system of clause 2, wherein the fused plurality of sensors further comprises at least one pairing of sensor types selected from the pairings consisting of:
a vibration sensor and a temperature sensor;
a vibration sensor and a pressure sensor;
a vibration sensor and an electric field sensor;
a vibration sensor and a heat flux sensor;
a vibration sensor and a galvanic sensor; and
a vibration sensor and a magnetic sensor.
21. The system of clause 1, wherein the sensor learning circuit is further structured to update the sensed parameter group by performing at least one operation selected from the operations consisting of:
updating a sensor selection of the sensed parameter group;
updating a sensor sampling rate of at least one sensor from the sensed parameter group;
updating a sensor resolution of at least one sensor from the sensed parameter group;
updating a storage value corresponding to at least one sensor from the sensed parameter group;
updating a priority corresponding to at least one sensor from the sensed parameter group; and
updating at least one of a sampling rate, sampling order, sampling phase, and a network path configuration corresponding to at least one sensor from the sensed parameter group.
22. The system of clause 21, wherein the pattern recognition circuit is further structured to determine the recognized pattern value by performing at least one operation selected from the operations consisting of:
determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest;
determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; and
updating the recognized pattern value in response to external feedback.
23. The system of clause 22, wherein the value of interest comprises at least one value selected from the values consisting of:
a virtual sensor output value;
a process prediction value;
a process state value;
a component prediction value;
a component state value; and
a model output value having the sensor data values from the fused plurality of sensors as an input.
24. The system of clause 2, wherein the pattern recognition circuit is further structured to access cloud-based data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system.
25. The system of clause 24, wherein the sensor learning circuit is further structured to access the cloud-based data comprising a second updated sensor parameter group corresponding to the at least one offset industrial system.
26. A method, comprising:
providing a plurality of sensors to an industrial system comprising a plurality of components, each of the plurality of sensors operatively coupled to at least one of the plurality of components;
interpreting a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising a fused plurality of sensors from the plurality of sensors;
determining a recognized pattern value comprising a secondary value determined in response to the plurality of sensor data values;
updating the sensed parameter group in response to the recognized pattern value; and
adjusting the interpreting the plurality of sensor data values in response to the updated sensed parameter group.
27. The method of clause 26, further comprising iteratively performing the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value.
28. The method of clause 27, further comprising determining the sensing performance value in response to determining at least one of:
a signal-to-noise performance for detecting a value of interest in the industrial system;
a network utilization of the plurality of sensors in the industrial system;
an effective sensing resolution for a value of interest in the industrial system;
a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors;
a calculation efficiency for determining the secondary value, wherein the calculation efficiency comprises at least one of: processor operations to determine the secondary value, memory utilization for determining the secondary value, a number of sensor inputs from the plurality of sensors for determining the secondary value, and supporting data long-term storage for supporting the secondary value;
one of an accuracy and a precision of the secondary value;
a redundancy capacity for determining the secondary value; and
a lead time value for determining the secondary value.
29. The method of clause 27, wherein updating the sensed parameter group comprises performing at least one operation selected from the operations consisting of:
updating a sensor selection of the sensed parameter group;
updating a sensor sampling rate of at least one sensor from the sensed parameter group;
updating a sensor resolution of at least one sensor from the sensed parameter group;
updating a storage value corresponding to at least one sensor from the sensed parameter group;
updating a priority corresponding to at least one sensor from the sensed parameter group; and
updating at least one of a sampling rate, sampling order, sampling phase, and a network path configuration corresponding to at least one sensor from the sensed parameter group.
30. The method of clause 27, wherein determining the recognized pattern value comprises performing at least one operation selected from the operations consisting of:
determining a signal effectiveness of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to a value of interest;
determining a sensitivity of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive confidence of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive delay time of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive accuracy of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest;
determining a predictive precision of at least one sensor of the sensed parameter group and the updated sensed parameter group relative to the value of interest; and
updating the recognized pattern value in response to external feedback.
31. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group, wherein the sensed parameter group comprises a fused plurality of sensors;
a means for recognizing a pattern value in response to the sensed parameter group; and
a means for updating the sensed parameter group in response to the recognized pattern value.
32. The system of clause 31, further comprising a means for iteratively updating the sensed parameter group.
33. The system of clause 32, further comprising a means for accessing at least one of external data and a second plurality of sensor data values corresponding to an offset industrial system, and wherein the means for iteratively updating the sensed parameter group is further responsive to the at least one of external data and the second plurality of sensor data values.
34. The system of clause 33, further comprising a means for accessing a second sensed parameter group corresponding to the offset industrial system, and wherein the means for iteratively updating is further responsive to the second sensed parameter group.
35. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values, wherein the recognized pattern value includes a secondary value comprising a value determined in response to the at least a portion of the plurality of sensors;
a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value;
wherein the sensor communication circuit is further structured to adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group; and
wherein the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value, wherein the sensing performance value comprises a signal-to-noise performance for detecting a value of interest in the industrial system.
36. The system of clause 35, wherein the sensed parameter group comprises a fused plurality of sensors, and wherein the secondary value comprises a value determined in response to the fused plurality of sensors.
37. The system of clause 36, wherein the secondary value comprises at least one value selected from the values consisting of:
a virtual sensor output value;
a process prediction value;
a process state value;
a component prediction value;
a component state value; and
a model output value having the sensor data values from the fused plurality of sensors as an input.
38. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values, wherein the recognized pattern value includes a secondary value comprising a value determined in response to the at least a portion of the plurality of sensors;
a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value;
wherein the sensor communication circuit is further structured to adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group; and
wherein the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value, wherein the sensing performance value comprises a network utilization of the plurality of sensors in the industrial system.
39. The system of clause 37, wherein the sensed parameter group comprises a fused plurality of sensors, and wherein the secondary value comprises a value determined in response to the fused plurality of sensors.
40. The system of clause 39, wherein the secondary value comprises at least one value selected from the values consisting of:
a virtual sensor output value;
a process prediction value;
a process state value;
a component prediction value;
a component state value; and
a model output value having the sensor data values from the fused plurality of sensors as an input.
41. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values, wherein the recognized pattern value includes a secondary value comprising a value determined in response to the at least a portion of the plurality of sensors;
a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value;
wherein the sensor communication circuit is further structured to adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group; and
wherein the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value, wherein the sensing performance value comprises an effective sensing resolution for a value of interest in the industrial system.
42. The system of clause 41, wherein the sensed parameter group comprises a fused plurality of sensors, and wherein the secondary value comprises a value determined in response to the fused plurality of sensors.
43. The system of clause 42, wherein the secondary value comprises at least one value selected from the values consisting of:
a virtual sensor output value;
a process prediction value;
a process state value;
a component prediction value;
a component state value; and
a model output value having the sensor data values from the fused plurality of sensors as an input.
44. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values, wherein the recognized pattern value includes a secondary value comprising a value determined in response to the at least a portion of the plurality of sensors;
a sensor learning circuit structured to update the sensed parameter group in response to the recognized pattern value;
wherein the sensor communication circuit is further structured to adjust the interpreting the plurality of sensor data values in response to the updated sensed parameter group; and
wherein the pattern recognition circuit and the sensor learning circuit are further structured to iteratively perform the determining the recognized pattern value and the updating the sensed parameter group to improve a sensing performance value, wherein the sensing performance value comprises a power consumption value for a sensing system in the industrial system, the sensing system including the plurality of sensors.
45. The system of clause 44, wherein the sensed parameter group comprises a fused plurality of sensors, and wherein the secondary value comprises a value determined in response to the fused plurality of sensors.
46. The system of clause 45, wherein the secondary value comprises at least one value selected from the values consisting of:
a virtual sensor output value;
a process prediction value;
a process state value;
a component prediction value;
a component state value; and
a model output value having the sensor data values from the fused plurality of sensors as an input.
Referencing
The example system 11000 further includes a sensor communication circuit 11018 (reference
In certain embodiments, sensor data values 11034 are provided to a data collector 11008, which may be in communication with multiple sensors 11006 and/or with a controller 11012. In certain embodiments, a plant computer 11010 is additionally or alternatively present. In the example system, the controller 11012 is structured to functionally execute operations of the sensor communication circuit 11018, pattern recognition circuit 11020, and/or the system characterization circuit 11022, and is depicted as a separate device for clarity of description. Aspects of the controller 11012 may be present on the sensors 11006, the data collector 11008, the plant computer 11010, and/or on a cloud computing device 11014. In certain embodiments, all aspects of the controller 11012 may be present in another device depicted on the system 11000. The plant computer 11010 represents local computing resources, for example processing, memory, and/or network resources, that may be present and/or in communication with the industrial system 11000. In certain embodiments, the cloud computing device 11014 represents computing resources externally available to the industrial system 11000, for example over a private network, intra-net, through cellular communications, satellite communications, and/or over the internet. In certain embodiments, the data collector 11008 may be a computing device, a smart sensor, a MUX box, or other data collection device capable to receive data from multiple sensors and to pass-through the data and/or store data for later transmission. An example data collector 11008 has no storage and/or limited storage, and selectively passes sensor data therethrough, with a subset of the sensor data being communicated at a given time due to bandwidth considerations of the data collector 11008, a related network, and/or imposed by environmental constraints. In certain embodiments, one or more sensors and/or computing devices in the system 11000 are portable devices—for example a plant operator walking through the industrial system may have a smart phone, which the system 11000 may selectively utilize as a data collector 11008, sensor 11006—for example to enhance communication throughput, sensor resolution, and/or as a primary method for communicating sensor data values 11034 to the controller 11012.
The example system 11000 further includes a pattern recognition circuit 11020 that determines a recognized pattern value 11028 in response to a least a portion of the sensor data values 11034, and a system characterization circuit 11022 that provides a system characterization value 11030 for the industrial system in response to the recognized pattern value 11028. The system characterization value 11030 includes any value determined from the pattern recognition operations of the pattern recognition circuit 11020, including determining that a system condition of interest is present, a component condition of interest is present, an abstracted condition of the system or a component is present (e.g., a product quality value; an operation cost value; a component health, wear, or maintenance value; a component capacity value; and/or a sensor saturation value) and/or is predicted to occur within a time frame (e.g., calendar time, operational time, and/or a process stage) of interest. Pattern recognition operations include determining that operations compatible with a previously known pattern, operations similar to a previously known pattern and/or extrapolated from previously known pattern information (e.g., a previously known pattern includes a temperature response for a first component, and a known or estimated relationship between components allows for a determination that a temperature for a second component will exceed a threshold based upon the pattern recognition for the first component combined with the known or estimated relationship).
Non-limiting descriptions of a number of examples of a system characterization value 11030 are described following. An example system characterization value 11030 includes a predicted outcome for a process associated with the industrial system—for example a product quality description, a product quantity description, a product variability description (e.g., the expected variability of a product parameter predicted according to the operating conditions of the system), a product yield description, a net present value (NPV) for a process, a process completion time, a process chance of completion success, and/or a product purity result. The predicted outcome may be a batch prediction (e.g., a single run, or an integer number of runs, of the process, and the associated predicted outcome), a time based prediction (e.g., the projected outcome of the process over the next day, the next three weeks, until a scheduled shutdown, etc.), a production defined prediction (e.g., the projected outcome over the next 1,000 units, over the next 47 orders, etc.), and/or a rate of change based outcome (e.g., projected for 3 component failures per month, an emissions output per year, etc.). An example system characterization value 11030 includes a predicted future state for a process associated with the industrial system—for example an operating temperature at a given future time, an energy consumption value, a volume in a tank, an emitted noise value at a school adjacent to the industrial system, and/or a rotational speed of a pump. The predicted future state may be time based (e.g., at 4 PM on Thursday), based on a state of the process (e.g., during the third stage, during system shutdown, etc.), and/or based on a future state of particular interest (e.g., peak energy consumption, highest temperature value, maximum noise value, time or process stage when a maximum number of personnel will be within 50 feet of a sensitive area, time or process stage when an aspect of the system redundancy is at a lowest point—e.g. for determining high risk points in a process, etc.). An example system characterization value 11030 includes a predicted off-nominal operation for the process associated with the industrial system—for example when a component capacity of the system will exceed nominal parameters (although, possibly, not experience a failure), when any parameter in the system will be three standard deviations away from normal operations, when a capacity of a component will be under-utilized, etc. An example system characterization value 11030 includes a prediction value for one of the number of components—for example an operating condition at a point in time and/or process stage. An example system characterization value 11030 includes a future state value for one of the number of components. The predicted future state of a component may be time based, based on a state of the process, and/or based on a future state of particular interest (e.g., a highest or lowest value predicted for the component). An example system characterization value 11030 includes an anticipated maintenance health state information for one of the number of components, including at a particular time, a process stage, a lowest value predicted until a next maintenance event, etc. An example system characterization value 11030 includes a predicted maintenance interval for at least one of the number of components (e.g., based on current usage, anticipated usage, planned process operations, etc.). An example system characterization value 11030 includes a predicted off-nominal operation for one of the number of components—for example at a selected time, a process stage, and/or a future state of particular interest. An example system characterization value 11030 includes a predicted fault operation for one of the plurality of components—for example at a selected time, a process stage, any fault occurrence predicted based on current usage, anticipated usage, planned process operations, and/or a future state of particular interest. An example system characterization value 11030 includes a predicted exceedance value for one of the number of components, where the exceedance value includes exceedance of a design specification, and/or exceedance of a selected threshold. An example system characterization value 11030 includes a predicted saturation value for one of the plurality of sensors for example at a selected time, a process stage, any saturation occurrence predicted based on current usage, anticipated usage, planned process operations, and/or a future state of particular interest.
Any values for the prediction value 11030 may be raw values (e.g., a temperature value), derivative values (e.g., a rate of change of a temperature value), accumulated values (e.g., a time spent above one or more temperature thresholds) including weighted accumulated values, and/or integrated values (e.g., an area over a temperature-time curve at a temperature value or temperature trajectory of interest). The provided examples list temperature, but any prediction value 11030 may be utilized, including at least vibration, system throughput, pressure, etc. In certain embodiments, combinations of one or more prediction values 11030 may be utilized
One of skill in the art, having the benefit of the disclosure herein, will recognize that combining prediction values 11030 can create particularly powerful combinations for system analysis, control, and risk management, that are specifically contemplated herein. For example, a first prediction value may indicate a time or process stage for a maximum flow rate through the system, and a second prediction value may determine the predicted state of one or more components of the system at that will be present at that time or process stage. In another example, a first prediction value indicates a lowest margin of the system in terms of capacity to deliver (e.g., by determining a point in the process wherein at least one component has a lowest operating margin, and/or where a group of components have a statistically lower operating margin due to the risk induced by a number of simultaneous low operating margins), and a second prediction value testing a system risk (e.g., loss of inlet water, loss of power, increase in temperature, change in environmental conditions that reduce or increase heat transfer, or that preclude the emission of certain effluents), and the combined risk of separate events can be assessed on the total system risk. Additionally, the prediction values may be operated with a sensitivity check (e.g., varying system conditions within margins to determine if some failure may occur), wherein the use of the prediction value allows for the sensitivity check to be performed with higher resolution at high risk points in the process.
An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036. External data 11036 includes, without limitation, data provided from outside the system 11000 and/or outside the controller 11012. Non-limiting example external data 11036 include entries from an operator (e.g., indicating a failure, a fault, and/or a service event). An example pattern recognition circuit 11020 further iteratively improves pattern recognition operations in response to the external data 11036 (e.g., where an outcome is known, such as a maintenance event, product quality determination, production outcome determination, etc., the detection of the recognized pattern value 11028 is thereby improved according to the conditions of the system before the known outcome occurred). Example and non-limiting external data 11036 includes data such as: an indicated process success value; an indicated process failure value; an indicated component maintenance event; an indicated component failure event; an indicated process outcome value; an indicated component wear value; an indicated process operational exceedance value; an indicated component operational exceedance value; an indicated fault value; and/or an indicated sensor saturation value.
An example system 11000 further includes a system collaboration circuit 11024 that interprets cloud-based data 11032 including a second number of sensor data values, the second number of sensor data values corresponding to at least one offset industrial system, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the cloud-based data 11032. An example pattern recognition circuit 11020 further iteratively improves pattern recognition operations in response to the cloud-based data 11032. An example sensed parameter group 11026 includes a triaxial vibration sensor, a vibration sensor and a second sensor that is not a vibration sensor, the second sensor being a digital sensor, and/or a number of analog sensors.
An example system includes an industrial system including an oil refinery. An example oil refinery includes one or more compressors for transferring fluids throughout the plant, and/or for pressurizing fluid streams (e.g., for reflux in a distillation column). Additionally or alternatively, the example oil refinery includes vacuum distillation, for example to fractionate hydrocarbons. The example oil refinery additionally includes various pipelines in the system for transferring fluids, bringing in feedstock, final product delivery, and the like. An example system includes a number of sensors configured to determine each aspect of a distillation column—for example temperatures of various fluid streams, temperatures and compositions of individual contact trays in the column, measurements of the feed and reflux, as well as of the effluent or separated products. The design of a distillation column is complex, and optimal design can depend upon the sizing of boilers, compressors, the contact conditions within the column, as well as the composition of feedstock which can vary significantly. Additionally, the optimal position for effective sensing of conditions in a pipeline can vary with fluid flow rates, environmental conditions (e.g., causing variation in heat transfer rates), the feedstock utilized, and other factors. Additionally, wear or loss of capability in a boiler, compressor, or other operating equipment can change the system response and capabilities, rendering a single point optimization, including where sensors should be positioned and how they should sample data, to be non-optimal as the system ages.
Provision of multiple sensors throughout the system can be costly, not necessarily because the sensors are expensive, but because they sensors provide data which may be prohibitive to transmit, store, and utilize. The example system includes providing a large number of sensors throughout the system, and predicting the future states of components, process variables, products, and/or emissions for the system. The example system utilizes a pattern recognition circuit to determine not only the future predicted state of parameters, but when the future predicted state of parameters will be of interest, and/or will combine with other future predicted state of parameters to create additional risks or opportunities.
Additionally, the system characterization circuit and the system collaboration circuit can improve predictions and/or system characterizations over time, and/or utilizing offset oil refineries, to more robustly make predictions or system characterizations, which can provide for earlier detection, longer term planning for overall enterprise optimization, and/or to allow the industrial system to operate closer to margins. If an unexpected operating condition occurs—for example an off-nominal operation of a compressor, the sensor collaboration circuit is capable to migrate the system prediction and improve the capability to detect the conditions that caused the unexpected operating condition in the system, and/or in offset systems. Additionally, alerts for the distillation column, based upon predictions indicating off-nominal operation, marginal operation, high risk operation, and/or upcoming maintenance or potential failures, can be readily prepared to provide visibility to risks that otherwise may not be apparent simply looking at system capacities and past experience without rigorous analysis.
Example sensor fusion operations for a refinery include vibration information combined with temperatures, pressures, and/or composition (e.g., to determine compressor performance); temperature and pressure, temperature and composition, and/or composition and pressure (e.g., to determine feedstock variance, contact tray performance, and/or a component failure).
An example refinery system includes storage tanks and/or boiler feed water. Example system determinations include a sensor fusion to determine a storage tank failure and/or off-nominal operation, such as through a temperature and pressure fusion, and/or a vibration determination with a non-vibration determination (e.g., detecting leaks, air in the system, and/or a feed pump issue). Certain further example system predictions include a sensor fusion to determine a boiler feed water failure, such as through a sensor fusion including flow rate, pressure, temperature, and/or vibration. Any one or more of these parameters can be utilized to predict a system leak, failure, wear of a feed pump, and/or scaling.
Similarly, an example industrial system includes a power generation system having a condensate and/or make-up water system, where a sensor fusion provides for a sensed parameter group and prediction of failures, maintenance, and the like. The system characterization circuit, utilizing sensor fusion and/or a continuous machine learning process, can predict failures, off-nominal operations, component health, and/or maintenance events for, without limitation, compressors, piping, storage tanks, and/or boiler feed water for an oil refinery.
An example industrial system includes an irrigation system for a field or a system of fields. Irrigations systems are subject to significant variability in the system (e.g., inlet pressures and/or water levels, component wear and maintenance) as well as environmental variability (e.g., types and distribution of crops planted, weather, soil moisture, humidity, seasonal variability in the sun, cloud coverage, and/or wind variance). Additionally, irrigation systems tend to be remotely located where high bandwidth network access, maintenance facilities, and/or even personnel for oversight are not readily available. An example system includes a multiplicity of sensors capable to enable prediction of conditions for the irrigation system, without requiring that all of the sensors transmit or store data on a continuous basis. The pattern recognition circuit can readily determine the most important set of sensors to effectively predict patterns and thus system conditions requiring a response (e.g., irrigation cycles, positioning, and the like). Additionally, alerts for remote facilities can be readily prepared, with confidence that the correct sensor package is in place for predicting an off-nominal condition (e.g., imminent failure or maintenance requirement for a pump). In certain embodiments, the system may determine an off-nominal process condition such as water feed availability being below normal (e.g., based upon recognized pattern conditions such as recent precipitation history, water production history from the irrigation system or other systems competing for the same water feed), structured news alerts or external data, etc., and update the sensed parameter group, for example to confirm the water feed availability (e.g., a water level sensor in a relevant location), to confirm that acceptable conditions are available that water delivery levels can be dropped (e.g., a humidity sensor, and/or a prompt to a user), and/or to confirm that sufficient available secondary sources are available (e.g., an auxiliary water level sensor).
An example industrial system includes a chemical or pharmaceutical plant. Chemical plants require specific operating conditions, flow rates, temperatures, and the like to maintain proper temperatures, concentrations, mixing, and the like throughout the system. In many systems, there are numerous process steps, and an off-nominal or uncoordinated operation in one part of the process can result in reduced yields, a failed process, and/or a significant reduction in production capacity as coordinated processes must respond (or as coordinated processes fail to respond). Accordingly, a very large number of systems are required to minimally define the system, and in certain embodiments a prohibitive number of sensors are required, from a data transmission and storage viewpoint, to keep sensing capability for a broad range of operating conditions. Additionally, the complexity of the system results in difficulty optimizing and coordinating system operations even where sufficient sensors are present. In certain embodiments, the pattern recognition circuit can predict the sensing parameter groups that provide high resolution understanding of the system, without requiring that all of the sensors store and transmit data continuously. Further, the pattern recognition circuit can highlight the predicted system risks and capacity limitations for upcoming process operations, where the risks are buried in the complex process. Accordingly, the can confidently be operated closer to margins, at a lower cost, and/or maintenance or system upgrades can be performed before failures or capacity limitations are experienced.
Further, the utilization of a sensor fusion provides for the opportunity to abstract desired predictions, such as “maximize quality” or “minimize and undesirable side reaction” without requiring a full understanding from the operator of which sensors and system conditions are most effective to achieve the abstracted desired output. Further, the predictive nature of the pattern recognition circuit allows for changes in the process to support the desired outcome to be implemented before the process is committed to a sub-optimal outcome. Example components in a chemical or pharmaceutical plan amenable to control and predictions based on operations of the pattern recognition circuit and/or a sensor fusion operation include an agitator, a pressure reactor, a catalytic reactor, and/or a thermic heating system. Example sensor fusion operations to determine sensed parameter groups and tune the pattern recognition circuit include, without limitation, a vibration sensor combined with another sensor type, a composition sensor combined with another sensor type, a flow rate determination combined with another sensor type, and/or a temperature sensor combined with another sensor type. For example, agitators are amenable to vibration sensing, as well as uniformity of composition detection (e.g., high resolution temperature), expected reaction rates in a properly mixed system, and the like. Catalytic reactors are amenable to temperature sensing (based on the reaction thermodynamics), composition detection (e.g., for expected reactants, as well as direct detection of catalytic material), flow rates (e.g., gross mechanical failure, reduced volume of beads, etc.), and/or pressure detection (e.g., indicative of or coupled with flow rate changes).
An example industrial system includes a food processing system. Example food processing systems include pressurization vessels, stirrers, mixers, and/or thermic heating systems. Control of the process is critical to maintain food safety, product quality, and product consistency. However, most input parameters to the food processing system are subject to high variability—for example basic food products are inherently variable as natural products, with differing water content, protein content, and aesthetic variation. Additionally, labor cost management, power cost management, and variability in supply water, etc., provide for a complex process where determination of the predictive variables, sensed parameters to determine these, and optimization of predicting in response to process variation are a difficult problem to resolve. Food processing systems are often cost conscious, and capital costs (e.g., for a robust network and computing system for optimization) are not readily incurred. Further, a food processing system may manufacture wide variance of products on similar or the same production facilities, for example to support an entire product line and/or due to seasonal variations, and accordingly a predictive operation for one process may not support another process well. An example system includes the pattern recognition circuit determining the sensing parameter groups that provide a strong signal response in target outcomes even in light of high variability in system conditions. The pattern recognition circuit can provide for numerous sensed group parameter options available for different process conditions without requiring extensive computing or data storage resources, and accordingly achieve relevant predictions for a wide variety of operating conditions. For example, control of and predictions for pressurization vessels, stirrers, mixers, and/or thermic heating systems are amenable to operations of the pattern recognition circuit, and/or a sensor fusion with a temperature determination combined with a non-temperature determination, a vibration determination combined with a non-vibration determination, and/or a heat map combined with a rate of change in the heat map and/or a non-heat map determination. An example system includes a pattern recognition circuit operation and/or a sensor fusion with a vibration determination and a non-vibration determination, wherein predictive information for a mixer and/or a stirrer is provided; and/or with a pressure determination, a temperature determination, and/or a non-pressure determination, wherein predictive information for a pressurization vessel is provided.
Referencing
An example procedure 11038 further includes the operation 11046 to provide the system characterization value by performing an operation such as: determining a predicted outcome for a process associated with the industrial system; determining a predicted future state for a process associated with the industrial system; determining a predicted off-nominal operation for the process associated with the industrial system; determining a prediction value for one of the plurality of components; determining a future state value for one of the plurality of components; determining an anticipated maintenance health state information for one of the plurality of components; determining a predicted maintenance interval for at least one of the plurality of components; determining a predicted off-nominal operation for one of the plurality of components; determining a predicted fault operation for one of the plurality of components; determining a predicted exceedance value for one of the plurality of components; and/or determining a predicted saturation value for one of the plurality of sensors.
An example procedure 11038 includes an operation 11050 to interpret external data and/or cloud-based data, and where the operation 11044 to determine the recognized pattern value is further in response to the external data and/or the cloud-based data. An example procedure 11038 includes an operation to iteratively improve pattern recognition operations in response to the external data and/or the cloud-based data, for example by operation 11048 to adjust the operation 11042 interpreting sensor values, such as by updating the sensed parameter group. The operation to iteratively improve pattern recognition may further include repeating operations 11042 through 11048, periodically, at selected intervals, in response to a system change, and/or in response to a prediction value of a component, process, or the system.
1. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising at least one sensor of the plurality of sensors;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values; and
a system characterization circuit structured to provide a system characterization value for the industrial system in response to the recognized pattern value.
2. The system of clause 1, wherein the system characterization value comprises at least one characterization value selected from the characterization values consisting of:
a predicted outcome for a process associated with the industrial system;
a predicted future state for a process associated with the industrial system;
a predicted off-nominal operation for the process associated with the industrial system;
3. The system of clause 1, wherein the system characterization value comprises at least one characterization value selected from the characterization values consisting of:
a prediction value for one of the plurality of components;
a future state value for one of the plurality of components;
an anticipated maintenance health state information for one of the plurality of components; and
a predicted maintenance interval for at least one of the plurality of components.
4. The system of clause 1, wherein the system characterization value comprises at least one characterization value selected from the characterization values consisting of:
a predicted off-nominal operation for one of the plurality of components;
a predicted fault operation for one of the plurality of components; and
a predicted exceedance value for one of the plurality of components.
5. The system of clause 1, wherein the system characterization value comprises a predicted saturation value for one of the plurality of sensors.
6. The system of clause 1, further comprising a system collaboration circuit structured to interpret external data, and wherein the pattern recognition circuit is further structured to determine the recognized pattern value further in response to the external data.
7. The system of clause 5, wherein the pattern recognition circuit is further structured to iteratively improve pattern recognition operations in response to the external data.
8. The system of clause 6, wherein the external data comprises at least one of:
an indicated component maintenance event;
an indicated component failure event;
an indicated component wear value;
an indicated component operational exceedance value; and
an indicated fault value.
9. The system of clause 6, wherein the external data comprises at least one of:
an indicated process failure value;
an indicated process success value;
an indicated process outcome value; and
an indicated process operational exceedance value.
10. The system of clause 6, wherein the external data comprises an indicated sensor saturation value.
11. The system of clause 1, further comprising a system collaboration circuit structured to interpret cloud-based data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system, and wherein the pattern recognition circuit is further structured to determine the recognized pattern value further in response to the cloud-based data.
12. The system of clause 11, wherein the pattern recognition circuit is further structured to iteratively improve pattern recognition operations in response to the cloud-based data.
13. The system of clause 1, wherein the sensed parameter group comprises a triaxial vibration sensor.
14. The system of clause 1, wherein the sensed parameter group comprises a vibration sensor and a second sensor that is not a vibration sensor.
15. The system of clause 14, wherein the second sensor comprises a digital sensor.
16. The system of clause 1, wherein the sensed parameter group comprises a plurality of analog sensors.
17. A method, comprising:
providing a plurality of sensors to an industrial system comprising a plurality of components, each of the plurality of sensors operatively coupled to at least one of the plurality of components;
interpreting a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising at least one sensor of the plurality of sensors;
determining a recognized pattern value in response to a least a portion of the plurality of sensor data values; and
providing a system characterization value for the industrial system in response to the recognized pattern value.
18. The method of clause 17, wherein providing the system characterization value comprises performing at least one operation selected from the operations consisting of:
determining a prediction value for one of the plurality of components;
determining a future state value for one of the plurality of components;
determining an anticipated maintenance health state information for one of the plurality of components;
and
determining a predicted maintenance interval for at least one of the plurality of components.
19. The method of clause 17, wherein providing the system characterization value comprises performing at least one operation selected from the operations consisting of:
determining a predicted outcome for a process associated with the industrial system;
determining a predicted future state for a process associated with the industrial system; and
determining a predicted off-nominal operation for the process associated with the industrial system.
20. The method of clause 17, wherein providing the system characterization value comprises performing at least one operation selected from the operations consisting of:
determining a predicted off-nominal operation for one of the plurality of components;
determining a predicted fault operation for one of the plurality of components; and
determining a predicted exceedance value for one of the plurality of components.
21. The method of clause 17, wherein providing the system characterization value comprises determining a predicted saturation value for one of the plurality of sensors.
22. The method of clause 17, further comprising interpreting external data, and wherein determining the recognized pattern value is further in response to the external data.
23. The method of clause 22, further comprising iteratively improving pattern recognition operations in response to the external data.
24. The method of clause 23, wherein interpreting the external data further includes at least one operation selected from the operations consisting of:
interpreting an indicated component maintenance event;
interpreting an indicated component failure event;
interpreting an indicated component wear value;
interpreting an indicated component operational exceedance value; and
interpreting an indicated fault value.
25. The method of clause 23, wherein interpreting the external data further includes at least one operation selected from the operations consisting of:
interpreting an indicated process success value;
interpreting an indicated process failure value;
interpreting an indicated process outcome value; and
interpreting an indicated process operational exceedance value.
26. The method of clause 23, wherein interpreting the external data further includes interpreting an indicated sensor saturation value.
27. The method of clause 16, further comprising interpreting cloud-based data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system, and wherein determining the recognized pattern value is further in response to the cloud-based data.
28. The method of clause 27, further comprising iteratively improving pattern recognition operations in response to the cloud-based data.
29. A system for data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising at least one sensor of the plurality of sensors;
a means for determining a recognized pattern value in response to at least a portion of the plurality of sensor data values; and
a means for providing a system characterization value for the industrial system in response to the recognized pattern value.
30. The system of clause 29, wherein the means for providing the system characterization value further comprises a means for performing at least one operation selected from the operations consisting of:
determining a predicted outcome for a process associated with the industrial system;
determining a predicted future state for a process associated with the industrial system; and
determining a predicted off-nominal operation for the process associated with the industrial system.
31. The system of clause 29, wherein the means for providing the system characterization value further comprises a means for performing at least one operation selected from the operations consisting of:
determining a prediction value for one of the plurality of components;
determining a future state value for one of the plurality of components;
determining an anticipated maintenance health state information for one of the plurality of components;
and
determining a predicted maintenance interval for at least one of the plurality of components.
32. The system of clause 29, wherein the means for providing the system characterization value further comprises a means for performing at least one operation selected from the operations consisting of:
determining a predicted off-nominal operation for one of the plurality of components;
determining a predicted fault operation for one of the plurality of components; and
determining a predicted exceedance value for one of the plurality of components.
33. The system of clause 29, wherein the means for providing the system characterization value further comprises a means for determining a predicted saturation value for one of the plurality of sensors.
34. The system of clause 29, further comprising a system collaboration circuit structured to interpret external data, and wherein the means for determining the recognized pattern value determines the recognized pattern value further in response to the external data.
35. The system of clause 34, further comprising a means for iteratively improving pattern recognition operations in response to the external data.
36. The system of clause 35, wherein the external data further comprises at least one of:
an indicated process success value;
an indicated process failure value; and
an indicated process outcome value.
37. The system of clause 35, wherein the external data further comprises at least one of:
an indicated component maintenance event;
an indicated component failure event; and
an indicated component wear value.
38. The system of clause 35, wherein the external data further comprises at least one of:
an indicated process operational exceedance value;
an indicated component operational exceedance value; and
an indicated fault value.
39. The system of clause 35, wherein the external data further comprises an indicated sensor saturation value.
40. The system of clause 29, further a system collaboration circuit structured to interpret cloud-based data comprising a second plurality of sensor data values, the second plurality of sensor data values corresponding to at least one offset industrial system, and wherein the means for determining the recognized pattern value determines the recognized pattern value further in response to the cloud-based data.
41. The system of clause 40, further comprising a means for iteratively improving pattern recognition operations in response to the cloud-based data.
42. A system for data collection in an industrial environment, the system comprising:
a distillation column comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising at least one sensor of the plurality of sensors;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values; and
a system characterization circuit structured to provide a system characterization value for the distillation column in response to the recognized pattern value.
43. The system of clause 42, wherein the plurality of components comprise a thermodynamic treatment component, and wherein the system characterization value comprises at least one value selected from the values consisting of:
determining a prediction value for the thermodynamic treatment component;
determining a future state value for the thermodynamic treatment component;
determining an anticipated maintenance health state information for the thermodynamic treatment component; and
determining a process rate limitation according to a capacity of the thermodynamic treatment component.
44. The system of clause 43, wherein the thermodynamic treatment component comprises at least one of a compressor or a boiler.
45. A system for data collection in an industrial environment, the system comprising:
a chemical process system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising at least one sensor of the plurality of sensors;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values; and
a system characterization circuit structured to provide a system characterization value for the chemical process system in response to the recognized pattern value.
46. The system of clause 45, wherein the chemical process system comprises one of a chemical plant, a pharmaceutical plant, or an oil refinery.
47. The system of clause 46, wherein the system characterization value comprises at least one value selected from the values consisting of:
a separation process value comprising at least one of a capacity value or a purity value;
a side reaction process value comprising a side reaction rate value; and
a thermodynamic treatment value comprising one of a capability, a capacity, and an anticipated maintenance health for a thermodynamic treatment component.
48. A system for data collection in an industrial environment, the system comprising:
an irrigation system comprising a plurality of components including a pump, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values in response to a sensed parameter group, the sensed parameter group comprising at least one sensor of the plurality of sensors;
a pattern recognition circuit structured to determine a recognized pattern value in response to a least a portion of the plurality of sensor data values; and
a system characterization circuit structured to provide a system characterization value for the irrigation system in response to the recognized pattern value.
49. The system of clause 48, wherein the system characterization value further comprises at least one of an anticipated maintenance health value for the pump and a future state value for the pump.
50. The system of clause 48, wherein the pattern recognition circuit further determines an off-nominal process condition in response to the at least a portion of the plurality of sensor data values, and wherein the sensor communication circuit is further structured to change the sensed parameter group in response to the off-nominal process condition.
51. The system of clause 50, wherein the off-nominal process condition comprises an indication of below normal water feed availability, and wherein the updated sensed parameter group comprises at least one sensor selected from the sensors consisting of: a water level sensor, a humidity sensor, and an auxiliary water level sensor.
As described elsewhere herein, feedback to various intelligent and/or expert systems, control systems (including remote and local systems, autonomous systems, and the like), and the like, which may comprise rule-based systems, model-based systems, artificial intelligence (AI) systems (including neural nets, self-organizing systems, and others described throughout this disclosure), and various combinations and hybrids of those (collectively referred to herein as the “expert system” except where context indicates otherwise), may include a wide range of information, including measures such as utilization measures, efficiency measures (e.g. power, financial such as reduction of costs), measures of success in prediction or anticipation of states (e.g. avoidance and mitigation of faults), productivity measures (e.g. workflow), yield measures, profit measures, and the like, as described herein. In embodiments feedback to the expert system may be industry-specific, domain-specific, factory-specific, machine-specific and the like.
Industry-specific feedback for the expert system may be offered by a third party, such as an RMO, manufacturer, one or more consortia, and the like, or may be generated by one or more elements of the subject system itself. Industry-specific feedback may be aggregated, such as into one or more data structures, wherein the data are aggregated at the component level, equipment level, factory/installation level, and/or industry level. Users of the data structure(s) may access data at any level (e.g. component, equipment, factory, industry, etc.). Users may search the data structure(s) for indicators/predictors based on or filtered by system conditions specific to their need, or update an indicator/predictor with proprietary data to customize the data structure to their industry. In embodiments, the expert system may be seeded with industry-specific feedback, such as in a deep learning fashion, to provide an anticipated outcome or state and/or to perform actions to optimize specific machines, devices, components, processes, and the like.
In embodiments, feedback provided to the expert system may be used in one or more smart bands to predict progress towards one or more goals. The expert system may use the feedback to determine a modification, alteration, addition, change, or the like to one or more components of the system that provided the feedback, as described elsewhere herein. Based on the industry-specific feedback, the expert system may alter an input, a way of treating or storing an input or output, a sensor or sensors used to provide feedback, an operating parameter, a piece of equipment used in the system, or any other aspect of the participants in the industrial system that gave rise to the feedback. As described elsewhere herein, the expert system may track multiple goals, such as with one or more smart bands. Industry-specific feedback may be used in or by the smart bands in predicting an outcome or state relating to the one or more goals, and to recommend or instruct a change that is directed in increasing a likelihood of achieving the outcome or state.
For example, a mixer may be used in a food processing environment or in a chemical processing environment, but the feedback that is relevant in the food processing plant (e.g. required sterilization temperatures, food viscosity, particle density (e.g. such as measured by an optical sensor), completion of cooking (e.g., completion of reactions involved in baking), sanitation (e.g., absence of pathogens) may be different than what is relevant in the chemical processing plant (e.g. impeller speed, velocity vectors, flow rate, absence of high contaminant levels, or the like). This industry specific feedback is useful in optimizing the operation of the mixer in its particular environment.
In another example, the expert system may use feedback from agricultural systems to train a model related to an irrigation system deployed in a field, wherein the industry-specific feedback relates to one or more of an amount of water used across the industry (e.g. such as measured by a flowmeter), a trend of water usage over a time period (e.g. such as measured by a flowmeter), a harvest amount (e.g. such as measured by a weight scale), an insect infestation (e.g. such as identified and/or measured by a drone imaging), a plant death (e.g. such as identified and/or measured by drone imaging), and the like.
In another example of a fluid flow system (e.g. fan, pump or compressor) controlling cooling in the manufacturing industry, the expert system may use feedback from manufacturing of components involving materials (e.g., polymers) that require cooling during the manufacturing process, such as one or more of quality of output product, strength of output product, flexibility of output product, and the like (e.g. such as measured by a suite of sensors, including densitometer, viscometer, size exclusion chromatograph, and torque meter). If the sensors indicate that the polymer is cooling too quickly during monomer conversion, the expert system may relay an instruction to one or more of a fan, pump, or compressor in the fluid flow system to decrease an aspect of its operation in order to meet a quality goal.
In another example of a reciprocating compressor operating in a refinery performing refinery processes (e.g. hydrotreating, hydrocracking, isomerization, reforming), the expert system may use feedback related to one or more of an amount of sulfur, nitrogen and/or aromatics downstream of the compressor (e.g. such as measured by a near infrared analyzer), the cetane/octane number or smoke point of a product (e.g. such as with an octane analyzer), the density of a product (e.g. such as measured by a densitometer), byproduct gas amounts (e.g., such as measured by an electrochemical gas sensor), and the like. In this example, as feedback is received during isomerization of butane to isobutene by an inline near IR analyzer measuring the amount and/or quality of isobutene, the expert system may determine that the performance of one or more components of the isomerization system, including the reciprocating compressor, should be altered in order to meet a production goal.
In another example of a vacuum distillation unit operating in a refinery, the expert system may use feedback related to an amount of raw gasoline recovered (e.g. such as by measuring the volume or composition of various fractions using IR), boiling point of recovered fractions (e.g. such as with a boiling point analyzer), a vapor cooling rate (e.g. such as measured by thermometer), and the like. In this example, as feedback is received during vacuum distillation to recover diesel, as the amounts recovered indicate off-nominal rations of production, the expert system may instruct the vacuum distillation unit to alter a feedstock source and initiate more detailed analysis of the prior feedstock.
In yet another example of a pipeline in a refinery, the expert system may use feedback related to flow type (e.g., bubble, stratified, slug, annular, transition, mist) of hydrocarbon products (e.g. such as measured by dye tracing), flow rate, vapor velocity (such as with a flow meter), vapor shear, and the like. In this example, as feedback is received during operation of the pipeline regarding the flow type and its rate, modifications may be recommended by the expert system to improve the flow through the pipeline.
In still another example of a paddle-type or anchor-type agitator/mixer in a pharmaceutical plant, the expert system may use feedback related to degree of mixing of high-viscosity liquids, heating of medium- to low-viscosity liquids, a density of the mixture, a growth rate of an organism in the mixture, and the like. In this example, as feedback is received during operation of the agitator that a bacterial growth rate is too high (such as measured with a spectrophotometer), the expert system may instruct the agitator to reduce its speed to limit the amount of air being added to the mixture or growth substrate.
In a further example of a pressure reactor in a chemical processing plant, the expert system may use feedback related to a catalytic reaction rate (such as measured by a mass spectrometer), a particle density (such as measured by a densitometer), a biological growth rate (such as measured by a spectrophotometer), and the like. In this example, as feedback is received during operation of the pressure reactor that the particle density and biological growth rate are off-nominal, the expert system may instruct the pressure reactor to modify one or more operational parameters, such as a reduction in pressure, an increase in temperature, an increase in volume of the reaction, and the like.
In another example of a gas agitator operating in a chemical processing plant, the expert system may use feedback related to effective density of a gassed liquid, a viscosity, a gas pressure, and the like, as measured by appropriate sensors or equipment. In this example, as feedback is received during operation of the gas agitator, the expert system may instruct the gas agitator to modify one or more operational parameters, such as to increase or decrease a rate of agitation.
In still another example of a pump blasting liquid type agitator in a chemical processing plant, the expert system may use feedback related to a viscosity of a mixture, an optical density of a growth medium, and a temperature of a solution. In this example, as feedback is received during operation of the agitator, the expert system may instruct the agitator to modify one or more operational parameters, such as to increase or decrease a rate of agitation and/or inject additional heat.
In yet another example of a turbine type agitator in a chemical processing plant, the expert system may use feedback related to a vibration noise, a reaction rate of the reactants, a heat transfer, or a density of a suspension. In this example, as feedback is received during operation of the agitator, the expert system may instruct the agitator to modify one or more operational parameters, such as to increase or decrease a rate of agitation and/or inject an additional amount of catalyst.
In yet another example of a static agitator mixing monomers in a chemical processing plant to produce a polymer, the expert system may use feedback related to the viscosity of the polymer, color of the polymer, reactivity of the polymer and the like to iterate to a new setting or parameter for the agitator, such as for example, a setting that alters the Reynolds number, an increase in temperature, a pressure increase, and the like.
In a further example of a catalytic reactor in a chemical processing plant, the expert system may use feedback related to a reaction rate, a product concentration, a product color, and the like. In this example, as feedback is received during operation of the catalytic reactor, the expert system may instruct the reactor to modify one or more operational parameters, such as to increase or decrease a temperature and/or inject an additional amount of catalyst.
In yet a further example of a thermic heating systems in a chemical processing or food plant, the expert system may use feedback related to BTUs out of the system, a flow rate, and the like. In this example, as feedback is received during operation of the thermic heating system, the expert system may instruct the system to modify one or more operational parameters, such as to change the input feedstock, to increase the flow of the feedstock, and the like.
In still a further example of using boiler feed water in a refinery, the expert system may use feedback related to an aeration level, a temperature, and the like. In this example, as feedback is received related to the boiler feed water, the expert system may instruct the system to modify one or more operational parameters of a boiler, such as to increase deaeration, to increase the flow of the feed water, and the like.
In still a further example of a storage tank in a refinery, the expert system may use feedback related to a temperature, a pressure, a flow rate out of the tank, and the like. In this example, as feedback is received related to the storage tank, the expert system may instruct the system to modify one or more operational parameters of, such as to increase cooling or heating begin agitation, and the like.
In an example of a condensate/make-up water system in a power station that condenses steam from turbines and recirculates it back to a boiler feeder along with make-up water, the expert system may use feedback related to measuring inward air leaks, heat transfer, and make-up water quality. In this example, as feedback is received related to the condensate/make-up water system, the expert system may instruct the system to increase a purification of the make-up water, bring a vacuum pump online, and the like.
In another example of a stirrer in a food plant, the expert system may use feedback related to a viscosity of the food, a color of the food, a temperature of the food, and the like. In this example, as feedback is received, the expert system may instruct the stirrer to speed up or slow down, depending on the predicted success in reaching a goal.
In another example of a pressure cooker in a food plant, the expert system may use feedback related to a viscosity of the food, a color of the food, a temperature of the food, and the like. In this example, as feedback is received, the expert system may instruct the pressure cooker to continue operating, increase a temperature, or the like, depending on the predicted success in reaching a goal.
In an embodiment, as depicted in
In embodiments, a system 11100 for data collection in an industrial environment may include a plurality of input sensors 11102 communicatively coupled to a controller 11106, a data collection circuit 11104 structured to collect output data 11108 from the input sensors, and a machine learning data analysis circuit 11110 structured to receive the output data 11108 and learn received output data patterns 11112 indicative of an outcome, wherein the machine learning data analysis circuit 11110 is structured to learn received output data patterns 11112 by being seeded with a model 11114 based on a utilization measure.
In embodiments, a system 11100 for data collection in an industrial environment may include a plurality of input sensors 11102 communicatively coupled to a controller 11106, a data collection circuit 11104 structured to collect output data 11108 from the input sensors, and a machine learning data analysis circuit 11110 structured to receive the output data 11108 and learn received output data patterns 11112 indicative of an outcome, wherein the machine learning data analysis circuit 11110 is structured to learn received output data patterns 11112 by being seeded with a model 11114 based on an efficiency measure.
In embodiments, a system 11100 for data collection in an industrial environment may include a plurality of input sensors 11102 communicatively coupled to a controller 11106, a data collection circuit 11104 structured to collect output data 11108 from the input sensors, and a machine learning data analysis circuit 11110 structured to receive the output data 11108 and learn received output data patterns 11112 indicative of an outcome, wherein the machine learning data analysis circuit 11110 is structured to learn received output data patterns 11112 by being seeded with a model 11114 based on a measure of success in prediction or anticipation of states.
In embodiments, a system 11100 for data collection in an industrial environment may include a plurality of input sensors 11102 communicatively coupled to a controller 11106, a data collection circuit 11104 structured to collect output data 11108 from the input sensors, and a machine learning data analysis circuit 11110 structured to receive the output data 11108 and learn received output data patterns 11112 indicative of an outcome, wherein the machine learning data analysis circuit 11110 is structured to learn received output data patterns 11112 by being seeded with a model 11114 based on a productivity measure.
1. A system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller;
a data collection circuit structured to collect output data from the input sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns indicative of an outcome,
wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model based on industry-specific feedback.
2. The system of clause 1, wherein the model is a physical model, an operational model, or a system model.
3. The system of clause 1, wherein the industry-specific feedback is a utilization measure.
4. The system of clause 1, wherein the industry-specific feedback is an efficiency measure.
5. The system of clause 4, wherein the efficiency measure is one of power and financial.
6. The system of clause 1, wherein the industry-specific feedback is a measure of success in prediction or anticipation of states.
7. The system of clause 6, wherein the measure of success is an avoidance and mitigation of faults.
8. The system of clause 1, wherein the industry-specific feedback is a productivity measure.
9. The system of clause 8, wherein the productivity measure is a workflow.
10. The system of clause 1, wherein the industry-specific feedback is a yield measure.
11. The system of clause 1, wherein the industry-specific feedback is a profit measure.
12. The system of clause 1, wherein the machine learning data analysis circuit is further structured to learn received output data patterns based on the outcome.
13. The system of clause 1, wherein the system keeps or modifies operational parameters or equipment.
14. The system of clause 1, wherein the controller adjusts the weighting of the machine learning data analysis circuit based on the learned received output data patterns or the outcome.
15. The system of clause 1, wherein the controller collects more/fewer data points from the input sensors based on the learned received output data patterns or the outcome.
16. The system of clause 1, wherein the controller changes a data storage technique for the output data based on the learned received output data patterns or the outcome.
17. The system of clause 1, wherein the controller changes a data presentation mode or manner based on the learned received output data patterns or the outcome.
18. The system of clause 1, wherein the controller applies one or more filters (low pass, high pass, band pass, etc.) to the output data.
19. The system of clause 1, wherein the system removes/re-tasks under-utilized equipment based on one or more of the learned received output data patterns and the outcome.
20. The system of clause 1, wherein the machine learning data analysis circuit comprises a neural network expert system.
21. The system of clause 1, wherein the input sensors measure vibration and noise data.
22. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of progress/alignment with one or more goals/guidelines.
23. The system of clause 22, wherein progress/alignment of each goal/guideline is determined by a different subset of the input sensors.
24. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of an unknown variable.
25. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of a preferred input among available inputs.
26. The system of clause 1, wherein the machine learning data analysis circuit is structured to learn received output data patterns indicative of a preferred input data collection band among available input data collection bands.
27. The system of clause 1, wherein the machine learning data analysis circuit is disposed in part on a machine, on one or more data collectors, in network infrastructure, in the cloud, or any combination thereof.
28. The system of clause 1, wherein the system is deployed on the data collection circuit.
29. The system of clause 1, wherein the system is distributed between the data collection circuit and a remote infrastructure.
30. The system of clause 1, wherein the industry-specific feedback includes an amount of power generated by a machine about which the input sensors provide information during operation of the machine.
31. The system of clause 1, wherein the industry-specific feedback includes a measure of the output of an assembly line about which the input sensors provide information.
32. The system of clause 1, wherein the industry-specific feedback includes a failure rate of units of product produced by a machine about which the input sensors provide information.
33. The system of clause 1, wherein the industry-specific feedback includes a fault rate of a machine about which the input sensors provide information.
34. The system of clause 1, wherein the industry-specific feedback includes the power utilization efficiency of a machine about which the input sensors provide information.
35. The system of clause 34, wherein the machine is a turbine.
36. The system of clause 34, wherein the machine is a transformer.
37. The system of clause 34, wherein the machine is a generator.
38. The system of clause 34, wherein the machine is a compressor.
39. The system of clause 34, wherein the machine stores energy.
40. The system of clause 1, wherein the machine includes power train components.
41. The system of clause 34, wherein the industry-specific feedback includes the rate of extraction of a material by a machine about which the input sensors provide information.
42. The system of clause 34, wherein the industry-specific feedback includes the rate of production of a gas by a machine about which the input sensors provide information.
43. The system of clause 34, wherein the industry-specific feedback includes the rate of production of a hydrocarbon product by a machine about which the input sensors provide information.
44. The system of clause 34, wherein the industry-specific feedback includes the rate of production of a chemical product by a machine about which the input sensors provide information.
45. The system of clause 1, wherein the data collection circuit comprises a data collector.
46. A system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller;
a data collection circuit structured to collect output data from the input sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns indicative of an outcome,
wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model based on a utilization measure.
47. A system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller;
a data collection circuit structured to collect output data from the input sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns indicative of an outcome,
wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model based on an efficiency measure.
48. A system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller;
a data collection circuit structured to collect output data from the input sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns indicative of an outcome,
wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model based on a measure of success in prediction or anticipation of states.
49. A system for data collection in an industrial environment, comprising:
a plurality of input sensors communicatively coupled to a controller;
a data collection circuit structured to collect output data from the input sensors; and
a machine learning data analysis circuit structured to receive the output data and learn received output data patterns indicative of an outcome,
wherein the machine learning data analysis circuit is structured to learn received output data patterns by being seeded with a model based on a productivity measure.
In embodiments, a system for data collection in an industrial environment may include an expert system graphical user interface in which a user may, by interacting with a graphical user interface element, set a parameter of a data collection band for collection by a data collector. The parameter may relate to at least one of setting a frequency range for collection and setting an extent of granularity for collection.
In embodiments, a system for data collection in an industrial environment may include an expert system graphical user interface in which a user may, by interacting with a graphical user interface element, identify a set of sensors among a larger set of available sensors for collection by a data collector. The user interface may include views of available data collectors, their capabilities, one or more corresponding smart bands, and the like.
In embodiments, a system for data collection in an industrial environment may include an expert system graphical user interface in which a user may, by interacting with a graphical user interface element, select a set of inputs to be multiplexed among a set of available inputs.
In embodiments, a system for data collection in an industrial environment may include an expert system graphical user interface in which a user may, by interacting with a graphical user interface element, select a component of an industrial machine displayed in the graphical user interface for data collection, view a set of sensors that are available to provide data about the industrial machine, and select a subset of sensors for data collection.
In embodiments, a system for data collection in an industrial environment may include an expert system graphical user interface in which a user may, by interacting with a graphical user interface element, view a set of indicators of fault conditions of one or more industrial machines, where the fault conditions are identified by application of an expert system to data collected from a set of data collectors. In embodiments, the fault conditions may be identified by manufacturers of portions of the one or more industrial machines. The fault conditions may be identified by analysis of industry trade data, third-party testing agency data, industry standards, and the like. In embodiments, a set of indicators of fault conditions of one or more industrial machines may include indicators of stress, vibration, heat, wear, ultrasonic signature, operational deflection shape, and the like, optionally including any of the widely varying conditions that can be sensed by the types of sensors described throughout this disclosure and the documents incorporated herein by reference.
In embodiments, a system for data collection in an industrial environment may include an expert graphical user interface that enables a user to select from a list of component parts of an industrial machine for establishing smart-band monitoring and in response thereto presents the user with at least one smart-band definition of an acceptable range of values for at least one sensor of the industrial machine and a list of correlated sensors from which data will be gathered and analyzed when an out of acceptable range condition is detected from the at least one sensor.
In embodiments, a system for data collection in an industrial environment may include an expert graphical user interface that enables a user to select from a list of conditions of an industrial machine for establishing smart-band monitoring and, in response thereto, presents the user with at least one smart-band definition of an acceptable range of values for at least one sensor of the industrial machine and a list of correlated sensors from which data will be gathered and analyzed when an out of acceptable range condition is detected from the at least one sensor.
In embodiments, a system for data collection in an industrial environment may include an expert graphical user interface that enables a user to select from a list of reliability measures of an industrial machine for establishing smart-band monitoring and, in response thereto, presents the user with at least one smart-band definition of an acceptable range of values for at least one sensor of the industrial machine and a list of correlated sensors from which data will be gathered and analyzed when an out of acceptable range condition is detected from the at least one sensor. In the system, the reliability measures may include one or more of industry average data, manufacturer's specifications, material specifications, recommendations, and the like. In embodiments, reliability measures may include measures that correlate to failures, such as stress, vibration, heat, wear, ultrasonic signature, operational deflection shape effect, and the like. In embodiments, manufacturer's specifications may include cycle count, working time, maintenance recommendations, maintenance schedules, operational limits, material limits, warranty terms, and the like. In embodiments, the sensors in the industrial environment may be correlated to manufacturer's specifications by associating a condition being sensed by the sensor to a specification type. In embodiments, a non-limiting example of correlating a sensor to a manufacturer's specification may include a duty cycle specification being correlated to a sensor that detects revolutions of a moving part. In embodiments, a temperature specification may correlate to a thermal sensor disposed to sense an ambient temperature proximal to the industrial machine.
In embodiments, a system for data collection in an industrial environment may include an expert graphical user interface that automatically creates a smart-band group of sensors disposed in the industrial environment in response to receiving a condition of the industrial environment for monitoring and an acceptable range of values for the condition.
In embodiments, a system for data collection in an industrial environment may include an expert graphical user interface that presents a representation of components of an industrial machine deployable in the industrial environment on an electronic display, and in response to a user selecting one or more of the components, searches a database of industrial machine failure modes for modes involving the selected component(s) and conditions associated with the failure mode(s) to be monitored, and further identifies a plurality of sensors in, on, or available to be disposed on the presented machine representation from which data will automatically be captured when the condition(s) to be monitored are detected to be outside of an acceptable range. In embodiments, the identified plurality of sensors includes at least one sensor through which the condition(s) will be monitored.
In embodiments, a system for data collection in an industrial environment may include a user interface for working with smart bands that may facilitate a user identifying sensors to include in a smart band group of sensors by selecting sensors presented on a map of a machine in the environment. A user may be guided to select among a subset of all possible sensors based on a smart band criteria, such as types of sensor data required for the smart band. A smart band may be focused on detecting trending conditions in a portion of the industrial environment; therefore, the user interface may direct the user choose among an identified subset of sensors, such as by only allowing sensors proximal to the smart band directed portion of the environment to be selectable in the user interface.
In embodiments, a smart band data collection configuration and deployment user interface may include views of components in an industrial environment and related available sensors. In embodiments, in response to selection of a component part of an industrial machine depicted in the user interface, sensors associated with smart band data collection for the component part may be highlighted so that the user may select one or more of the sensors. User selection in this context may comprise a verification of an automatic selection of sensors, or manually identifying sensors to include in the smart band sensor group.
In embodiments, in response to selection of a smart band condition, such as trending of bearing temperature, a user interface for smart band configuration and use may automatically identify and present sensors that contribute to smart band analysis for the condition. A user may be responsive to this presentation of sensors, confirm or otherwise acknowledge one or more sensors individually or as a set to be included in the smart band data collection group.
In embodiments, a smart band user interface may present locations of industrial machines in an industrial environment on a map. The locations may be annotated with indicators of smart band data collection templates that are configured for collecting smart band data for the machines at the annotated locations. The locations may be color coded to reflect a degree of smart band coverage for a machine at the location. In embodiments, a location of a machine with a high degree of smart band coverage may be colored green, whereas a location of a machine with low smart band coverage may be colored red or some other contrasting color. Other annotations, such as visual annotations may be used. A user may select a machine at a location and by dragging the selected machine to a location of a second machine, effectively configure smart bands for the second machine that correspond to smart bands for the first machine. In this way, a user may configure several smart band data collection templates for a newly added machine or a new industrial environment and the like.
In embodiments, various configurations and selections of smart bands may be stored for use throughout a data collection platform, such as for selecting templates for sensing, templates for routing, provisioning of devices and the like, as well as for direct the placement of sensors, such as by personnel or by machines, such as autonomous or remote-control drones.
In embodiments, a smart band user interface may present a map of an industrial environment that may include industrial machines, machine-specific data collectors, mobile data collectors (robotic and human), and the like. A user may view a list of smart band data collection actions to be performed and may select a data collection resource set to undertake the collection. In an example, a guided mobile robot may be equipped with data collection systems for collecting data for a plurality of smart band data sets. A user may view an industrial environment with which the robot is associated and assign the robot to perform a smart band data collection activity by selecting the robot, a smart band data collection template, and a location in the industrial environment, such as a machine or a part of a machine. The user interface may provide a status of the collection undertaking so that the user can be informed when the data collection is complete.
In embodiments, a smart band operation management user interface may include presentation of smart band data collection activity, analysis of results, actions taken based on results, suggestions for changes to smart band data collection (e.g., addition of sensors to a smart band collection template, increasing duration of data collection for a template-specific collection activity), and the like. The user interface may facilitate “what if” type analysis by presenting potential impacts on reliability, costs, resource utilization, data collection tradeoffs, maintenance schedule impacts, risk of failure (increase/decrease), and the like in response to a user's attempt to make a change to a smart band data collection template, such as a user relaxing a threshold for performing smart band data collection and the like. In embodiments, a user may select or enter a target budget for preventive maintenance per unit time (e.g., per month, quarter, and the like) into the user interface and an expert system of the user interface may recommend a smart band data collection template and thresholds for complying with the budget.
In embodiments, a smart band user interface may facilitate a user configuring a system for data collection in an industrial environment for smart band data gathering. The user interface may include display of industrial machine components, such as motors, linkages, bearings, and the like that a user may select. In response to such a selection, an expert system may work with the user interface to present a list of potential failure conditions related to the part to monitor. The user may select one or more conditions to monitor. The user interface may present the conditions to monitor as a set that the user may be asked to approve. The user may indicate acceptance of the set or of select conditions in the set monitor. As a follow-on to a user selection/approval of one or more conditions to monitor, the user interface may display a map of relevant sensors available in the industrial environment for collecting data as a smart band group of sensors. The relevant sensors may be associated with one or more parts (e.g., the part(s) originally selected by the user), one or more failure conditions, and the like.
In embodiments, the expert system may compare the relevant sensors in the environment to a preferred set of sensors for smart band monitoring of the failure condition(s) and provide feedback to the user, such as a confidence factor for performing smart band monitoring based on the available sensors for the failure condition(s). The user may evaluate the failure condition and smart band analysis information presented and may take an action in the user interface, such as approving the relevant sensors. In response, a smart band data collection template for configuring the data collection system may be created. In embodiments, a smart band data collection template may be created independently of a user approval. In such embodiments, the user may indicate explicitly or implicitly via approval of the smart band analysis information an approval of the created template.
In embodiments, a smart band user interface may work with an expert system to present candidate portions of an industrial machine in an industrial environment for smart band condition monitoring based on information such as manufacturer's specifications, statistical information derived from real-world experience with similar industrial machines, and the like. In embodiments, the user interface may permit a user to select certain aspects of the smart band data collection and analysis process including, for example, a degree of reliability/failure risk to monitor (e.g., near failure, best performance, industry average, and the like). In response thereto, the expert system may adjust an aspect of the smart band analysis, such as a range of acceptable value to monitor, a monitor frequency, a data collection frequency, a data collection amount, a priority for the data collection activity (e.g., effectively a priority of a template for data collection for the smart band), weightings of data from sensors (e.g., specific sensors in the group, types of sensors, and the like).
In embodiments, a smart bands user interface may be structured to allow a user to let an expert system recommend one or more smart bands to implement based on a range of comparative data that the user might prioritize, such as industry average data, industry best data, near-by comparable machines, most similarly configured machines, and the like. Based on the comparative data weighting, the expert system may use the user interface to recommend one or more smart band templates that align with the weighting to the user, who may take an action in the user interface, such as approving one or more of the recommended templates for use.
In embodiments, a user interface for configuring arrangement of sensors in an industrial environment may include recommendations by industrial environment equipment suppliers (e.g., manufacturers, wholesalers, distributors, dealers, third-party consultants, and the like) of group(s) of sensors to include for performing smart band analysis of components of the industrial equipment. The information may be presented to a user as data collection template(s) that the user may indicate as being accepted/approved, such as by positioning a graphic representing a template(s) over a portion of the industrial equipment.
In embodiments, a smart band discovery portal may facilitate sharing of smart band related information, such as recommendations, actual use cases, results of smart band data collection and processing, and the like. The discovery portal may be embodied as a panel in a smart band user interface.
In embodiments, a smart band assessment portal may facilitate assessment of smart band-based data collection and analysis. Content that may be presented in such a portal may include depictions of uses of existing smart band templates for one or more industrial machines, industrial environments, industries, and the like. A value of a smart band maybe ascribed to each smart band in the portal based, for example, on historical use and outcomes. A smart band assessment portal may also include visualization of candidate sensors to include in a smart band data collection template based on a range of factors including ascribed value, preventive maintenance costs, failure condition being monitored, and the like.
In embodiments, a smart bands graphical user interface associated with a system for data collection in an industrial environment may be deployed for industrial components, such as of factory-based air conditioning units. A user interface of a system for data collection for smart band analysis of air conditioning units may facilitate graphical configuration of smart band data collection templates and the like for specific air conditioning system installations. In embodiments, major components of an air conditioning system, such as a compressor, condenser, heat exchanger, ducting, coolant regulators, filters, fans, and the like along with corresponding sensors for a particular installation of the air conditioning system may be depicted in a user interface. A user may select one or more of these components in the user interface for configuring a system for smart band data collection. In response to the user selecting, for example, a coolant compressor, sensors associated with the compressor may be automatically identified in the user interface. The user may be presented with a recommended data collection template to perform smart band data collection for the selected compressor. Alternatively, the user may request a candidate collection template from a community of smart band users, such as through a smart band template sharing panel of the user interface. Once a template is selected, the user interface may offer the user customization options, such as frequency of collection, degree of reliability to monitor, and the like. Upon final acceptance of the template, the user interface may interact with a data collection system of the installed air conditioning system (if such a system is available) to implement the data collection template and provide an indication to the user of the result of implementing the template. In response thereto, the user may make a final approval of the template for use with the air conditioning unit.
In embodiments, a smart bands graphical user interface associated with a system for data collection in an industrial environment may be deployed for oil and gas refinery-based chillers. A user interface of a system for data collection for smart band analysis of refinery-based chillers may facilitate graphical configuration of smart band data collection templates and the like for specific refinery-based chiller installations. In embodiments, major components of a refinery-based chiller including heat exchangers, compressors, water regulators and the like along with corresponding sensors for the particular installation of the refinery-based chiller may be depicted in a user interface. A user may select one or more of these components in the user interface for configuring a system for smart band data collection. In response to the user selecting, for example, water regulators, sensors associated with the water regulators may be automatically identified in the user interface. The user may be presented with a recommended data collection template to perform smart band data collection for the selected component. Alternatively, the user may request a candidate collection template from a community of smart band users, such as through a smart band template sharing panel of the user interface. Once a template is selected, the user interface may offer the user customization options, such as frequency of collection, degree of reliability to monitor, and the like. Upon final acceptance of the template, the user interface may interact with a data collection system of the installed refinery-based chiller (if such a system is available) to implement the data collection template and provide an indication to the user of the result of implementing the template. In response thereto, the user may make a final approval of the template for use with the refinery-based chiller.
In embodiments, a smart bands graphical user interface associated with a system for data collection in an industrial environment may be deployed for automotive production line robotic assembly systems. A user interface of a system for data collection for smart band analysis of production line robotic assembly systems may facilitate graphical configuration of smart band data collection templates and the like for specific production line robotic assembly system installations. In embodiments, major components of a production line robotic assembly system including motors, linkages, tool handlers, positioning systems and the like along with corresponding sensors for the particular installation of the production line robotic assembly system may be depicted in a user interface. A user may select one or more of these components in the user interface for configuring a system for smart band data collection. In response to the user selecting, for example, robotic linkage sensors associated with the robotic linkages may be automatically identified in the user interface. The user may be presented with a recommended data collection template to perform smart band data collection for the selected component. Alternatively, the user may request a candidate collection template from a community of smart band users, such as through a smart band template sharing panel of the user interface. Once a template is selected, the user interface may offer the user customization options, such as frequency of collection, degree of reliability to monitor, and the like. Upon final acceptance of the template, the user interface may interact with a data collection system of the installed production line robotic assembly system (if such a system is available) to implement the data collection template and provide an indication to the user of the result of implementing the template. In response thereto, the user may make a final approval of the template for use with the production line robotic assembly system.
In embodiments, a smart bands graphical user interface associated with a system for data collection in an industrial environment may be deployed for automotive production line robotic assembly systems. A user interface of a system for data collection for smart band analysis of production line robotic assembly systems may facilitate graphical configuration of smart band data collection templates and the like for specific production line robotic assembly system installations. In embodiments, major components of construction site boring machinery, such as the cutter head, which itself is a subsystem that may have many components, control systems, debris handling and conveying components, precast concrete delivery and installation subsystems and the like along with corresponding sensors for the particular installation of the production line robotic assembly system may be depicted in a user interface. A user may select one or more of these components in the user interface for configuring a system for smart band data collection. In response to the user selecting, for example, debris handling components sensors associated with the debris handling components, such as a conveyer may be automatically identified in the user interface. The user may be presented with a recommended data collection template to perform smart band data collection for the selected component. Alternatively, the user may request a candidate collection template from a community of smart band users, such as through a smart band template sharing panel of the user interface. Once a template is selected, the user interface may offer the user customization options, such as frequency of collection, degree of reliability to monitor, and the like. Upon final acceptance of the template, the user interface may interact with a data collection system of the installed production line robotic assembly system (if such a system is available) to implement the data collection template and provide an indication to the user of the result of implementing the template. In response thereto, the user may make a final approval of the template for use with the production line robotic assembly system.
Referring to
1. A system comprising: a user interface comprising: a selectable graphical element that facilitates selection of a representation of a component of an industrial machine from an industrial environment in which a plurality of sensors are deployed from which a data collection system collects data for the system for which the user interface enables interaction; and selectable graphical elements representing a portion of the plurality of sensors that facilitate selection of a sensors to form a data collection subset of sensors in the industrial environment.
2. The system of clause 1, wherein selection of sensors to form a data collection subset results in a data collection template adapted to facilitate configuring the data routing and collection system for collecting data from the data collection subset of sensors.
3. The system of clause 1, wherein the user interface comprises an expert system that analyzes a user selection of a graphical element that facilitates selection of a component and adjusts the selectable graphical elements representing a portion of the plurality of sensors to activate only sensors associated with a component associated with the selected graphical element.
4. The system of clause 1, wherein the selectable graphical element that facilitates selection of a component of an industrial machine further facilitates presentation of a plurality of data collection templates associated with the component.
5. The system of clause 1, wherein the portion of the plurality of sensors comprises a smart band group of sensors.
6. The system of clause 5, wherein the smart band group of sensors comprises sensors for a component of the industrial machine selected by the selectable graphical element.
7. A system comprising: an expert graphical user interface comprising representations of a plurality of components of an industrial machine from an industrial environment in which a plurality of sensors are deployed from which a data collection system collects data for the system for which the user interface enables interaction, wherein at least one representation of the plurality of components is selectable by a user in the user interface; a database of industrial machine failure modes; and a database searching facility that searches the database of failure modes for modes that correspond to a user selection of a component of the plurality of components.
8. The system of clause 7, comprising a database of conditions associated with the failure modes.
9. The system of clause 8, wherein the database of conditions includes a list of sensors in the industrial environment associated with the condition.
10. The system of clause 9, wherein the database searching facility further searches the database of conditions for sensors that correspond to at least one condition and indicates the sensors in the graphical user interface.
11. The system of clause 7, wherein the user selection of a component of the plurality of components causes a data collection template for configuring the data routing and collection system to automatically collect data from sensors associated with the selected component.
12. A method comprising: presenting in an expert graphical user interface a list of reliability measures of an industrial machine; facilitating user selection of one reliability measure from the list; presenting a representation of a smart band data collection template associated with the selected reliability measure; and in response to a user indication of acceptance of the smart band data collection template, configuring a data routing and collection system to collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.
13. The method of clause 12, wherein the reliability measures include one or more of industry average data, manufacturer's specifications, manufacturer's material specifications, and manufacturer's recommendations.
14. The method of clause 13, wherein include the manufacturer's specifications include at least one of cycle count, working time, maintenance recommendations, maintenance schedules, operational limits, material limits, and warranty terms.
15. The method of clause 12, wherein the reliability measures correlate to failures selected from the list consisting of stress, vibration, heat, wear, ultrasonic signature, and operational deflection shape effect.
16. The method of clause 12, further comprising correlating sensors in the industrial environment to manufacturer's specifications.
17. The method of clause 16, wherein correlating comprises matching a duty cycle specification to a sensor that detects revolutions of a moving part.
18. The method of clause 16, wherein correlating comprises matching a temperature specification with a thermal sensor disposed to sense an ambient temperature proximal to the industrial machine.
19. The method of clause 16, further comprising dynamically setting the acceptable range of data values based on a result of the correlating.
20. The method of clause 16, further comprising automatically determining the one of the plurality of sensors for detecting the data value outside of the acceptable range based on a result of the correlating.
Back calculation, such as for determining possible root causes of failures and the like, may benefit from a graphical approach that facilitates visualizing an industrial environment, machine, or portion thereof marked with indications of information sources that may provide data, such as sensors and the like related to the failure. A failed part, such as a bearing may be associated with other parts, such as shaft, motor, and the like. Sensors for monitoring conditions of the bearing and the associated parts may provide information that could indicate a potential source of failure. Such information may also be useful to suggest indicators, such as changes in sensor output, to monitor to avoid the failure in the future. A system that facilitates a graphical approach for back-calculation may interact with sensor data collection and analysis systems to at least partially automate aspects related to data collection and processing determined from a back-calculation process.
In embodiments, a system for data collection in an industrial environment in may include a user interface in which portions of an industrial machine associated with a condition of interest, such as a failure condition, are presented on an electronic display along with sensor data types contributing to the condition of interest, data collection points (e.g., sensors) associated with the machine portions that monitor the data types, a set of data from the data collection points that was collected and used to determine the condition of interest, and an annotation of sensors that delivered exceptional data, such as data that is out of an acceptable range, and the like that may have been used to determine the condition of interest. The user interface may access a description of the machine that facilitates determining and visualizing related components, such as bearing, shafts, brakes, rotors, motor housings, and the like that contribute to a function, such as rotating a turbine. The user interface may also access a data set that relates sensors disposed in and about the machine with the components. Information in the data set may include descriptions of the sensors, their function, a condition that each senses, typical or acceptable ranges of values output from the sensors, and the like. The information in the data set may also identify a plurality of potential pathways in a system for data collection in an industrial environment for sensor data to be delivered to a data collector. The user interface may also access a data set that may include data collection templates used to configure a data collection system for collecting data from the sensors to meet specific purposes (e.g., to collect data from groups of sensors into a sensor data set suitable for determining a condition of the machine, such as a degree of slippage of the shaft relative to the motor, and the like).
In embodiments, a method of back-calculation for determining candidate sources of data collection for data that contributes to a condition of an industrial machine may include following routes of data collection determined from a configuration and operational template of a data collection system for collecting data from sensors deployed in the industrial machine that was in place when the contributing data was collected. A configuration and operational template may describe signal path switching, multiplexing, collection timing, and the like for data from a group of sensors. The group of sensors may be local to a component, such as a bearing, or more regionally distributed, such as sensors that capture information about the bearing and its related components. In embodiments, a data collection template may be configured for collecting and processing data to detect a particular condition of the industrial machine. Therefore, templates may be correlated to conditions so that performing back-calculation of a condition of interest can be guided by the correlated template. Data collected based on the template may be examined and compared to acceptable ranges of data for various sensors. Data that is outside of an acceptable range may indicate potential root causes of an unacceptable condition. In embodiments, a suspect source of data collection may be determined from the candidate sources of data collection based on a comparison of data collected from the candidate data sources with an acceptable range of data collected from each candidate data source. Visualizing these back-calculation based signal paths, candidate sensors, and suspect data sources provides a user with valuable insights into possible root causes of failures and the like.
In embodiments, a method for back-calculation may include visualizing route(s) of data that contribute to a fault condition detected in an industrial environment by applying back-calculation to determine sources of the contributed data with the visualizing appearing as highlighted data paths in a visual representation of the data collection system in the industrial machine. In embodiments, determining sources of data may be based on a data collection and processing template for the fault condition. The template may include a configuration of a data collection system when data from the determined sources was collected with the system.
When failures occur, or conditions of a portion of a machine in an industrial environment reach a critical point prior to failure, such as may be detected during preventive maintenance and the like, back-calculation may be useful in determining information to gather that might help avoid the failure and/or improve system performance by, for example avoiding substantive degradation in component operation. Visualizing data collection sources, components related to a condition, algorithms that may determine the potential onset of the condition and the like may facilitate preparation of data collection templates for configuring data sensing, routing, and collection resources in a system for data collection in an industrial environment. In embodiments, configuring a data collection template for a system for collecting data in an industrial environment may be based on back-calculations applied to machine failures that identify candidate conditions to monitor for avoiding the machine failures. The resulting template may identify sensors to monitor, sensor data collection path configuration, frequency and amount of data to collect, acceptable levels of sensor data and the like. With access to information about the machine, such as which parts closely relate to others and sensors that collected data from parts in the machine, a data collection system configuration template may be automatically generated when a target component is identified.
In embodiments, a user interface may include a graphical display of data sources as a logical arrangement of sensors that may contribute data to a calculation of a condition of a machine in an industrial environment. A logical arrangement may be based on sensor type, data collection template, condition, algorithm for determining a condition, and the like. In an example, a user may wish to view all temperature sensors that may contribute to a condition, such as a failure of a part in an industrial environment. A user interface may communicate with a database of machine related information, such as parts that relate to a condition, sensors for those parts, and types of those sensors to determine the subset of sensors that measure temperature. The user interface may highlight those sensors. The user interface may activate selectable graphical elements for those sensors that, when selected by the user may present data associated with those sensors, such as sensor type, ranges of data collected, acceptable ranges, actual data values collected for a given condition, and the like, such as in a pop-up panel or the like Similar functionality of the user interface may apply to physical arrangements of sensors, such as all sensors associated with a motor, boring machine cutting head, wind turbine, and the like.
In embodiments, third-parties, such as component manufacturers, remote maintenance organizations and the like may benefit from access to back-calculation visualization Permitting third parties to have access to back-calculation information, such as sensors that contributed unacceptable data values to a calculation of a condition, visualization of sensor positioning, and the like may be an option that a user can exercise in a user interface for a graphical approach to back-calculations as described herein. A list of manufacturers of machines, sub-systems, individual components, sensors, data collection systems, and the like may be presented along with remote maintenance organizations, and the like in a portion of a user interface. A user of the interface may select one or more of these third-parties to grant access to at least a portion of the available data and visualizations. Selecting one or more of these third-parties may also present statistical information about the party, such occurrences and frequency of access to data to which the party is granted access, request from the party for access, and the like.
In embodiments, visualization of back-calculation analysis may be combined with machine learning so that back-calculations and their visualizations may be used to learn potential new diagnoses for conditions, such as failure conditions, to learn new conditions to monitor, and the like. A user may interact with the user interface to provide the machine learning techniques feedback to improve results, such as indicating a success or failure of an attempt to prevent failures through specific data collection and processing solutions (e.g., templates), and the like.
In embodiments, methods and systems of back-calculation of data collected with a system for data collection in an industrial environment may be applied to concrete pouring equipment in a construction site application. Concrete pouring equipment may comprise several active components including mixers that may include water and aggregate supply systems, mixing control systems, mixing motors, directional controllers, concrete sensors and the like, concrete pumps, delivery systems, flow control as well as on/off controls, and the like. Back-calculation of failure or other conditions of active or passive components of a concrete pouring equipment may benefit from visualization of the equipment, its components, sensors and other points where data is collected (e.g., controllers and the like). Visualizing data/conditions collected from sensors associated with concrete pumps and the like when performing back-calculation of a flow rate failure condition may inform the user of a conditions of the pump that may contribute to the flow rate failure. Flow rate may decrease contemporaneously with an increase in temperature of the pump. This may be visualized by, for example, presenting the flow rate sensor data and the pump temperature sensor data in the user interface. This correlation may be noted by an expert system or by a user observing the visualization and corrective action may be taken.
In embodiments, methods and systems of back-calculation of data collected with a system for data collection in an industrial environment may be applied to digging and extraction systems in a mining application. Digging and extraction systems may comprise several active sub-systems including cutting heads, pneumatic drills, jack hammers, excavators, transport systems, and the like. Back-calculation of failure or other conditions of active or passive components of digging and extraction systems may benefit from visualization of the equipment, its components, sensors and other points where data is collected (e.g., controllers and the like). Visualizing data/conditions collected from sensors associated with pneumatic drills and the like when performing back-calculation of a pneumatic line failure condition may inform the user of a conditions of the drill that may contribute to the line failure. Line pressure may increase contemporaneously with a change of a condition of the drill. This may be visualized by, for example, presenting the line pressure sensor data and data from sensors associated with the drill in the user interface. This correlation may be noted by an expert system or by a user observing the visualization and corrective action may be taken.
In embodiments, methods and systems of back-calculation of data collected with a system for data collection in an industrial environment may be applied to cooling towers in an oil and gas production environment. Cooling towers may comprise several active components including feedwater systems, pumps, valves, temperature-controlled operation, storage systems, mixing systems and the like. Back-calculation of failure or other conditions of active or passive components of cooling towers may benefit from visualization of the equipment, its components, sensors and other points where data is collected (e.g., controllers and the like). Visualizing data/conditions collected from sensors associated with the cooling towers and the like when performing back-calculation of a circulation pump failure condition may inform the user of a conditions of the cooling towers that may contribute to the pump failure. Temperature of the feedwater may increase contemporaneously with a decrease in output of the circulation pump. This may be visualized by, for example, presenting the feed water temperature sensor data and the pump output rate sensor data in the user interface. This correlation may be noted by an expert system or by a user observing the visualization and corrective action may be taken.
In embodiments, methods and systems of back-calculation of data collected with a system for data collection in an industrial environment may be applied to circulation water systems in a power generation application. Circulation water systems may comprise several active components including, pumps, storage systems, water coolers, and the like. Back-calculation of failure or other conditions of active or passive components of circulation water systems may benefit from visualization of the equipment, its components, sensors and other points where data is collected (e.g., controllers and the like). Visualizing data/conditions collected from sensors associated with water coolers and the like when performing back-calculation of a circulation water temperature failure condition may inform the user of a conditions of the cooler that may contribute to the temperature condition failure. Circulation temperature may increase contemporaneously with an increase of core water cooler temperature. This may be visualized by, for example, presenting the circulation water temperature sensor data and the water cooler temperature sensor data in the user interface. This correlation may be noted by an expert system or by a user observing the visualization and corrective action may be taken.
Referring to
1. A system comprising:
a user interface of a system adapted to collect data in an industrial environment;
the user interface comprising:
a plurality of graphical elements representing mechanical portions of an industrial machine, wherein the plurality of graphical elements are associated with a condition of interest generated by a processor executing a data analysis algorithm;
a plurality of graphical elements representing data collectors in a system adapted for collecting data in an industrial environment that collected data used in the data analysis algorithm; and
a plurality of graphical elements representing sensors used to capture the data used in the data analysis algorithm, wherein graphical elements for sensors that provided data that was outside of an acceptable range of data values are indicated through a visual highlight in the user interface.
2. The system of clause 1, wherein the condition of interest is selected from a list of conditions of interest presented in the user interface.
3. The system of clause 1, wherein the condition of interest is a mechanical failure of at least one of the mechanical portions of the industrial machine.
4. The system of clause 1, wherein the mechanical portions comprise at least one of a bearing, shaft, rotor, housing, and linkage of the industrial machine.
5. The system of clause 1, wherein the acceptable range of data values is available for each sensor.
6. The system of clause 1, further comprising highlighting data collectors that collected the data that was outside of the acceptable range of data values.
7. The system of clause 1, further comprising a data collection system configuration template that facilitates configuring the data collection system to collect the data for calculating the condition of interest.
8. A method of determining candidate sources of a condition of interest comprising:
identifying a data collection template for configuring data routing and collection resources in a system adapted to collect data in an industrial environment, wherein the template was used to collect data that contributed to a calculation of the condition of interest;
determining paths from data collectors for the collected data to sensors that produced the collected data by analyzing the data collection template;
comparing data collected by the sensors with acceptable ranges of data values for data collected by the sensors; and
highlighting, in an electronic user interface that depicts the industrial environment and at least one of the sensors, at least one sensor that produced data that contributed to the calculation of the condition of interest that is outside of the acceptable range of data for that sensor.
9. The method of clause 8, wherein the condition of interest is a failure condition.
10. The method of clause 8, wherein the data collection template comprises configuration information for at least one of an analog crosspoint switch, a multiplexer, a hierarchical multiplexer, a sensor, a collector, and a data storage facility of the system adapted to collect data in the industrial environment.
11. The method of clause 8, wherein the highlighting in the industrial environment comprises highlighting he at least one sensor, and at least one route of data from the sensor to a data collector of the system for data collection in the industrial environment.
12. The method of clause 8, wherein comparing data collected by the sensors with acceptable ranges of data values comprises comparing data collected by each sensor with an acceptable range of data values that is specific to each sensor.
13. The method of clause 8, wherein the calculation of the condition of interest comprises calculating a trend of data from at least one sensor.
14. The method of clause 8, wherein the acceptable range of values comprises a trend of data values.
15. A method of visualizing routes of data that contribute to a condition of interest that is detected in an industrial environment, the method comprising:
applying back calculation to the condition of interest to determine a data collection system configuration template associated with the condition of interest;
analyzing the template to determine a configuration of the data collection system for collecting data for detecting the condition of interest;
presenting, in an electronic user interface, a map of the data collection configured by the template; and
highlighting, in the electronic user interface, routes in the data collection system that reflect paths of data from at lest one sensor to at least one data collector for data that contributes to calculating the condition of interest.
16. The method of clause 15 wherein the data collection system configuration template comprises configuration information for at least one resource deployed in the data collection system selected from the list consisting of an analog crosspoint switch, a multiplexer, a hierarchical multiplexer, a data collector, and a sensor.
17. The method of clause 15, further comprising generating a target diagnosis for the condition of interest by applying machine learning to the back calculation.
18. The method of clause 15, further comprising highlighting in the electronic user interface, sensors that produce data used in calculating the condition of interest that is outside of an acceptable range of data values for the sensor.
19. The method of clause 15, wherein the condition of interest is selected from a list of conditions of interest presented in the user interface.
20. The system of clause 15, wherein the condition of interest is a mechanical failure of at least one mechanical portion of the industrial environment.
21. The system of clause 15, wherein the mechanical portions comprise at least one of a bearing, shaft, rotor, housing, and linkage of the industrial environment.
In embodiments, a system for data collection in an industrial environment may route data from a plurality of sensors in the industrial environment to wearable haptic stimulators that present the data from the sensors as human detectable stimuli including at least one of tactile, vibration, heat, sound, and force. In embodiments, the haptic stimulus represents an effect on the machine resulting from the sensed data. In embodiments, a bending effect may be presented as bending a finger of a haptic glove. In embodiments, a vibrating effect may be presented as vibrating a haptic arm band. In embodiments, a heating effect may be presented as an increase in temperature of a haptic wrist band. In embodiments, an electrical effect (e.g., over voltage, current, and others) may be presented as a change in sound of a phatic audio system.
In embodiments, an industrial machine operator haptic user interface may be adapted to provide haptic stimuli to the operator that is responsive to the operator's control of the machine, wherein the stimuli indicate an impact on the machine as a result of the operator's control and interaction with objects in the environment as a result thereof. In embodiments, sensed conditions of the machine that exceed an acceptable range may be presented to the operator through the haptic user interface. In embodiments, the sensed conditions of the machine that are within an acceptable range may not be presented to the operator through the haptic user interface. In embodiments, the sensed conditions of the machine that are within an acceptable range may presented as natural language representations of confirmation of the operator control. In embodiments, at least a portion of the haptic user interface is worn by the operator. In embodiments, a wearable haptic user interface device may include force exerting devices along the outer legs of a device operator's uniform. When a vehicle that the operator is controlling approaches an obstacle along a lateral side of the vehicle, an inflatable bellows may be inflated, exerting pressure against the leg of the operator closest to the side of the vehicle approaching the obstacle. The bellows may continue to be inflated, thereby exerting additional pressure on the operator's leg that is consistent with the proximity of the obstacle. The pressure may be pulsed when contact with the obstacle is imminent. In another example, an arm band of an operator may vibrate in coordination with vibration being experienced by a portion of the vehicle that the operator is controlling. These are merely examples and not intended to be limiting or restrictive of the ways in which a wearable haptic feedback user device may be controlled to indicate conditions that are sensed by a system for data collection in an industrial environment.
In embodiments, a haptic user interface safety system worn by a user in an industrial environment may be adapted to indicate proximity to the user of equipment in the environment by stimulating a portion of the user with at least one of pressure, heat, impact, electrical stimuli and the like, the portion of the user being stimulated may be closest to the equipment. In embodiments, at least one of the type, strength, duration, and frequency of the stimuli is indicative of a risk of injury to the user.
In embodiments, a wearable haptic user interface device, that may be worn by a user in an industrial environment, may broadcast its location and related information upon detection of an alert condition in the industrial environment. The alert condition may be proximal to the user wearing the device, or not proximal but related to the user wearing the device. A user may be an emergency responder, so the detection of a situation requiring an emergency responded, the user's haptic device may broadcast the user's location to facilitate rapid access to the user or by the user to the emergency location. In embodiments, an alert condition may be determined from monitoring industrial machine sensors may be presented to the user as haptic stimuli, with the severity of the alert corresponding to a degree of stimuli. In embodiments, the degree of stimuli may be based on the severity of the alert, the corresponding stimuli may continue, be repeated, or escalate, optionally including activating multiple stimuli concurrently, send alerts to additional haptic users, and the like until an acceptable response is detected, e.g., through the haptic UI. The wearable haptic user device may be adapted to communicate with other haptic user devices to facilitate detecting the acceptable response.
In embodiments, a wearable haptic user interface for use in an industrial environment may include gloves, rings, wrist bands, watches, arm bands, head gear, belts, necklaces, shirts (e.g., uniform shirt), footwear, pants, ear protectors, safety glasses, vests, overalls, coveralls, and any other article of clothing or accessory that can be adapted to provide haptic stimuli.
In embodiments, wearable haptic device stimuli may be correlated to a sensor in an industrial environment. Non-limiting examples include a vibration of a wearable haptic device in response to vibration detected in an industrial environment; increasing or decreasing the temperature of a wearable haptic device in response to a detected temperature in an industrial environment; producing sound that changes in pitch responsively to changes in a sensed electrical signal, and the like. In embodiments, a severity of wearable haptic device stimuli may correlate to an aspect of a sensed condition in the industrial environment. Non-limiting examples include moderate or short-term vibration for a low degree of sensed vibration; strong or long-term vibration stimulation for an increase in sensed vibration; aggressive, pulsed, and/or multi-mode stimulation for a high amount of sensed vibration. Wearable haptic device stimuli may also include lighting (e.g., flashing, color changes, and the like), sound, odor, tactile output, motion of the haptic device (e.g., inflating/deflating a balloon, extension/retraction of an articulated segment, and the like), force/impact, and the like.
In embodiments, a system for data collection in an industrial environment may interface with wearable haptic feedback user devices to relay data collected from fuel handling systems in a power generation application to the user via haptic stimulation Fuel handling for power generation may include solid fuels, such as woodchips, stumps, forest residue, sticks, energy willow, peat, pellets, bark, straw, agro biomass, coal and solid recovery fuel Handling systems may include receiving stations that may also sample the fuel, preparation stations that may crush or chip wood-based fuel or shred waste-based fuel. Fuel handling systems may include storage and conveying systems, feed and ash removal systems and the like. Wearable haptic user interface devices may be used with fuel handling systems by providing an operator feedback on conditions in the handling environment that the user is otherwise isolated from. Sensors may detect operational aspects of a solid fuel feed screw system. Conditions like screw rotational rate, weight of the fuel, type of fuel, and the like may be converted into haptic stimuli to a user while allowing the user to use his hands and provide his attention to operate the fuel feed system.
In embodiments, a system for data collection in an industrial environment may interface with wearable haptic feedback user devices to relay data collected from suspension systems of a truck and/or vehicle application to the user via haptic stimulation Haptic simulation may be correlated with conditions being sensed by the vehicle suspension system. In embodiments, road roughness may be detected and converted into vibration-like stimuli of a wearable haptic arm band. In embodiments, suspension forces (contraction and rebound) may be converted into stimuli that present a scaled down version of the forces to the user through a wearable haptic vest.
In embodiments, a system for data collection in an industrial environment may interface with wearable haptic feedback user devices to relay data collected from hydroponic systems in an agriculture application to the user via haptic stimulation. In embodiments, sensors in hydroponic systems, such as temperature, humidity, water level, plant size, carbon dioxide/oxygen levels, and the like may be converted to wearable device haptic stimuli. As an operator wearing haptic feedback clothing walks through a hydroponic agriculture facility, sensors proximal to the operator may signal to the haptic feedback clothing relevant information, such as temperature or a measure of actual temperature versus desired temperature that the haptic clothing may convert into haptic stimuli. In an example, a wrist band may include a thermal stimulator that can change temperature quickly to track temperature data or a derivative thereof from sensors in the agriculture environment. As a user walks through the facility, the haptic feedback wristband may change temperature to indicate a degree to which proximal temperatures are complying with expected temperatures.
In embodiments, a system for data collection in an industrial environment may interface with wearable haptic feedback user devices to relay data collected from robotic positioning systems in an automated production line application to the user via haptic stimulation Haptic feedback may include receiving a positioning system indicator of accuracy and converting it to an audible signal when the accuracy is acceptable, and another type of stimuli when the accuracy is not acceptable.
Referring to
1. A system for data collection in an industrial environment, comprising:
a plurality of wearable haptic stimulators that produce stimuli selected from the list of stimuli consisting of tactile, vibration, heat, sound, force, odor, and motion;
a plurality of sensors deployed in the industrial environment to sense conditions in the environment;
a processor logically disposed between the plurality of sensors and the wearable haptic stimulators, the processor receiving data from the sensors representative of the sensed condition, determining at least one haptic stimulation that corresponds to the received data, and sending at least one signal for instructing the wearable haptic stimulators to produce the at least one stimulation.
2. The system of clause 1, wherein the haptic stimulation represents an effect on a machine in the industrial environment resulting from the condition.
3. The system of clause 2, wherein a bending effect is presented as bending a haptic device.
4. The system of clause 2, wherein a vibrating effect is presented as vibrating a haptic device.
5. The system of clause 2, wherein a heating effect is presented as an increase in temperature of a haptic device.
6. The system of clause 2, wherein an electrical effect is presented as a change in sound produced by a haptic device.
7. The system of clause 2, wherein at least one of the plurality of wearable haptic stimulators are selected from the list consisting of a glove, ring, wrist band, wrist watch, arm band, head gear, belt, necklace, shirt, foot wear, pants, overalls, coveralls, and safety goggles.
8. The system of clause 2, wherein the at least one signal comprises an alert of a condition of interest in the industrial environment.
9. The system of clause 8, wherein the at least one stimulation produced in response to the alert signal is repeated by at least one of the plurality of wearable haptic stimulators until an acceptable response is detected.
10. An industrial machine operator haptic user interface that is adapted to provide the operator haptic stimuli responsive to the operator's control of the machine based on at least one sensed condition of the machine that indicates an impact on the machine as a result of the operator's control and interaction with objects in the environment as a result thereof.
11. The user interface of clause 10, wherein a sensed condition of the machine that exceeds an acceptable range of data values for the condition is presented to the operator through the haptic user interface.
12. The user interface of clause 10, wherein a sensed condition of the machine that is within an acceptable range of data values for the condition is presented as natural language representations of confirmation of the operator control via an audio haptic stimulator.
13. The user interface of clause 10, wherein at least a portion of the haptic user interface is worn by the operator.
14. The system of clause 10, wherein a vibrating sensed condition is presented as vibrating stimulation by the haptic user interface.
15. The system of clause 10, wherein a temperature-based sensed condition is presented as heat stimulation by the haptic user interface.
16. A haptic user interface safety system worn by a user in an industrial environment, wherein the interface is adapted to indicate proximity to the user of equipment in the environment by haptic stimulation via a portion of the haptic user interface that is closest to the equipment, wherein at least one of the type, strength, duration, and frequency of the stimulation is indicative of a risk of injury to the user.
17. The haptic user interface of clause 16, wherein the haptic stimulation is selected from a list consisting of pressure, heat, impact, and electrical stimulation.
18. The haptic user interface of clause 16 wherein the haptic user interface further comprises a wireless transmitter that broadcasts a location of the user.
19. The haptic user interface of clause 18, wherein the wireless transmitter broadcasts a location of the user in response to indicating proximity of the user to the equipment.
20. The haptic user interface of clause 16, wherein the proximity to the user of equipment in the environment is based on sensor data provided to the haptic user interface from a system adapted to collect data in an industrial environment, wherein the system is adapted based on a data collection template associated with a user safety condition in the industrial environment.
In embodiments, a system for data collection in an industrial environment may facilitate presenting a graphical element indicative of industrial machine sensed data on an augmented reality (AR) display. The graphical element may be adapted to represent a position of the sensed data on a scale of acceptable values of the sensed data. The graphical element may be positioned proximal to a sensor detected in the field of view being augmented that captured the sensed data in the AR display. The graphical element may be a color and the scale may be a color scale ranging from cool colors (e.g., greens, blues) to hot colors (e.g., yellow, red) and the like. Cool colors may represent data values closer to the midpoint of the acceptable range and the hot colors representing data values close to or outside of a maximum or minimum value of the range.
In embodiments, a system for data collection in an industrial environment may present, in an AR display, data being collected from a plurality of sensors in the industrial environment as one of a plurality graphical effects (e.g., colors in a range of colors) that correlate the data being collected from each sensor to a scale of values within an acceptable range compared to values outside of the acceptable range. In embodiments, the plurality of graphical effects may overlay a view of the industrial environment and placement of the plurality of graphical effects may correspond to locations in the view of the environment at which a sensor is located that is producing the corresponding sensor data. In embodiments, a first set of graphical effects (e.g., hot colors) represent components for which multiple sensors indicate values outside acceptable ranges.
In embodiments, a system for data collection in an industrial environment may facilitate presenting, in an AR display information being collected by sensors in the industrial environment as a heat map overlaying a visualization of the environment so that regions of the environment with sensor data suggestive of a greater potential of failure are overlaid with a graphic effect that is different than regions of the environment with sensor data suggestive of a lesser potential of failure. In embodiments, the heat map is based on data currently being sensed. In embodiments, the heat map is based on data from prior failures. In embodiments, the heat map is based on changes in data from an earlier period, such as data that suggest an increased likelihood of machine failure. In embodiments, the heat map is based on a preventive maintenance plan and a record of preventive maintenance in the industrial environment.
In embodiments, a system for data collection in an industrial environment may facilitate presenting information being collected by sensors in the industrial environment as a heat map overlaying a view of the environment, such as a live view as may be presented in an AR display. Such a system may include presenting an overlay that facilitates a call to action, wherein the overlay is associated with a region of the heat map. The overlay may comprise a visual effect of a part or subsystem of the environment on which the action is to be performed. In embodiments, the action to be performed is maintenance related and may be part-specific.
In embodiments, a system for data collection in an industrial environment may facilitate updating, in an AR view of a portion of the environment, a heat map of aspects of the industrial environment based on a change to operating instructions for at least one aspect of a machine in the industrial environment. The heat map may represent compliance with operational limits for portions of machines in the industrial environment. In embodiments, the heat map may represent a likelihood of component failure as a result of the change to operation instructions.
In embodiments, a system for data collection in an industrial environment may facilitate presenting, as a heat map in an AR view of a portion of the environment, a degree or measure of coverage of sensors in the industrial environment for a data collection template that identifies select sensors in the industrial environment for a data collection activity.
In embodiments, a system for data collection in an industrial environment may facilitate displaying a heat map overlaying a view, such as a live view, of an industrial environment of failure-related data for various portions of the environment. The failure-related data may comprise a difference between an actual failure rate of the various portions and another failure rate. Another failure rate may be a rate of failure of comparable portions elsewhere in the environment, and/or average failure rate of comparable portions across a plurality of environments, such as an industry average, manufacturer failure rate estimate, and the like.
In embodiments, a system for data collection in an industrial environment may facilitate displaying a heat map related to data collected from robotic arms and hands for production line robotic handling in an augmented reality view of a portion of the environment. A heat map related to data collected from robotic arms and hands may represent data from sensors disposed in, for example, the fingers of a robotic hand Sensor may collect data, such as applied pressure when pinching an object, resistance (e.g., responsive to a robotic touch) of an object, multi-axis forces presented to the finger as it performs an operation, such as holding a tool and the like, temperature of the object, total movement of the finger from initial point of contact until a resistance threshold is met, and other hand position/use conditions. Heat maps of this data may be presented in an augmented reality view of a robotic production environment so that a user may make a visual assessment of, for example, how the relative positioning of the robotic fingers impacts the object being handled
In embodiments, a system for data collection in an industrial environment may facilitate displaying a heat map related to data collected from linear bearings for production line robotic handling in an augmented reality view of a portion of the environment. Linear bearings, as with most bearings, may not be visible while in use. However, assessing their operation may benefit from representing data from sensors that capture information about the bearings while in use in an augmented reality display. In embodiments, sensors may be placed to detect forces being placed on portions of the bearings by the rotating member or elements that the bearings support. These forces may be presented as heat maps that correspond to relative forces, on a visualization of the bearings in an augmented reality view of a robot handling machine that uses linear bearings.
In embodiments, a system for data collection in an industrial environment may facilitate displaying a heat map related to data collected from boring machinery for mining in an augmented reality view of a portion of the environment. Boring machinery, and in particular multi-tip circular boring heads may experience a range of rock formations at the same time. Sensors may be placed proximal to each boring tip that may detect forces experienced by the tips. The data may be collected by a system adapted to collect data in an industrial environment and provided to an augmented reality system that may display the data as heat maps or the like in a view of the boring machine.
Referring to
1. An augmented reality (AR) system in which industrial machine sensed data is presented in a view of the industrial machine as heat maps of data collected from sensors in the view, wherein the heat maps are positioned proximal to a sensor capturing the sensed data that is visible in the AR display.
2. The system of clause 1, wherein the heat maps are based on a comparison of real time data collected from sensors with an acceptable range of values for the data.
3. The system of clause 1, wherein the heat maps are based on trends of sensed data.
4. The system of clause 1, wherein the heat maps represent a measure of coverage of sensors in the industrial environment in response to a condition of interest that is calculated from data collected by sensors in the industrial environment.
5. The system of clause 1, wherein the heat maps of data collected from sensors in the view is based on data collected by a system adapted to collect data in the industrial environment by routing data from a plurality of sensors to a plurality of data collectors via at least one of an analog crosspoint switch, a multiplexer, and a hierarchical multiplexer.
6. The system of clause 1, wherein the heat maps present different collected data values as different colors.
7. The system of clause 1, wherein data collected from a plurality of sensors is combined to produce a heat map.
8. A system for data collection in an industrial environment, comprising:
an augmented reality display that presents data being collected from a plurality of sensors in the industrial environment as one of a plurality of colors, wherein the colors correlate the data being collected from each sensor to a color scale with cool colors mapping to values of the data within an acceptable range and hot colors mapping to values of the data outside of the acceptable range, wherein the plurality of colors overlay a view of the industrial environment and placement of the plurality of colors corresponds to locations in the view of the environment at which a sensor is located that is producing the corresponding sensor data.
9. The system of clause 8, wherein hot color represent components for which multiple sensors indicate values outside typical ranges.
10. The system of clause 8, wherein the plurality of colors are based on a comparison of real time data collected from sensors with an acceptable range of values for the data.
11. The system of clause 8, wherein the plurality of colors is based on trends of sensed data.
12. The system of clause 8, wherein the plurality of colors represent a measure of coverage of sensors in the industrial environment in response to a condition of interest that is calculated from data collected by sensors in the industrial environment.
13. A method comprising, presenting information being collected by sensors in an industrial environment as a heat map overlaying a view of the environment so that regions of the environment with sensor data suggestive of a greater potential of failure are overlaid with a heat map that is different than regions of the environment with sensor data suggestive of a lesser potential of failure.
14. The method of clause 13, wherein the heat map is based on data currently being sensed.
15. The method of clause 13, wherein the heat map is based on data from prior failure data.
16. The method of clause 13, wherein the heat map is based on changes in data from an earlier period that suggest an increased likelihood of machine failure.
17. The method of clause 13, wherein the heat map is based on a preventive maintenance plan and a record of preventive maintenance in the industrial environment.
18. The method of clause 13, wherein the heat map represents an actual failure rate versus a reference failure rate.
19. The method of clause 18, wherein the reference failure rate is an industry average failure rate.
20. The method of clause 18, wherein the reference failure rate is a manufacturer's failure rate estimate.
In embodiments, a system for data collection and visualization thereof in an industrial environment may include an augmented reality and/or virtual reality (AR/VR) display in which data values output by sensors disposed in a field of view in the AR/VR display are displayed with visual attributes that indicate a degree of compliance of the data to an acceptable range or values for the sensed data. In embodiments, the visual attributes may provide near real-time portrayal of trends of the sensed data and/or of derivatives thereof. In embodiments, the visual attributes may be the actual data being captured, or the derived data, such as a trend of the data and the like.
In embodiments, a system for data collection and visualization thereof in an industrial environment may include an AR/VR display in which trends of data values output by sensors disposed in a field of view in the AR/VR are displayed with visual attributes that indicate a degree of severity of the trend. In embodiments, other data or analysis that could be displayed may include: data from sensors that exceed an acceptable range, data from sensors that are part of a smart band selected by the user, data from sensors that are monitored for triggering a smart band collection action, data from sensors that sense an aspect of the environment that meets a preventive maintenance criteria, such as a PM action is upcoming soon, a PM action was recently performed or is overdue for PM. Other data for such AR/VR visualization may include data from sensors for which an acceptable range has recently been changed, expanded, narrowed and the like. Other data for such AR/VR visualization that may be particularly useful for an operator of an industrial machine (digging, drilling, and the like) may include analysis of data from sensors, such as for example impact on an operating element (torque, force, strain, and the like).
In embodiments, a system for data collection and visualization thereof in an industrial environment that may include presentation of visual attributes that represent collected data in an AR/VR environment may do so for pumps in a mining application. Mining application pumps may provide water and remove liquefied waste from a mining site. Pump performance may be monitored by sensors detecting pump motors, regulators, flow meters, and the like. Pump performance monitoring data may be collected and presented as a set of visual attributes in an augmented reality display. In an example, pump motor power consumption, efficiency, and the like may be displayed proximal to a pump viewed through an augmented reality display.
In embodiments, a system for data collection and visualization thereof in an industrial environment that may include presentation of visual attributes that represent collected data in an AR/VR environment may do so for energy storage in a power generation application. Power generation energy storage may be monitored with sensors that capture data related to storage and use of stored energy. Information such as utilization of individual energy storage cells, energy storage rate (e.g., battery charging and the like), stored energy consumption rate (e.g., KWH being supplied by an energy storage system), storage cell status, and the like may be captured and converted into augmented reality viewable attributes that may be presented in an augmented reality view of an energy storage system.
In embodiments, a system for data collection and visualization thereof in an industrial environment that may include presentation of visual attributes that represent collected data in an AR/VR environment may do so for feed water systems in a power generation application. Sensors may be disposed in an industrial environment, such as power generation for collecting data about feed water systems. Data from those sensors may be captured and processed by the system for data collection. Results of this processing may include trends of the data, such as feed water cooling rates, flow rates, pressure and the like. These trends may be presented on an augmented reality view of a feed water system by applying a map of sensors with physical elements visible in the view and then retrieving data from the mapped sensors. The retrieved data (and derivatives thereof) may be presented in the augmented reality view of the feed water system.
Referring to
Clause 1 A system for data collection and visualization thereof in an industrial environment in which data values output by sensors disposed in a field of view in an electronic display are displayed in the electronic display with visual attributes that indicate a degree of compliance of the data to an acceptable range or values for the sensed data.
Clause 2. The system of clause 1, wherein the view in the electronic display is a view in an augmented reality display of the industrial environment.
Clause 3. The system of clause 1, wherein the visual attributes are indicative of a trend of the sensed data over time relative to the acceptable range.
Clause 4. The system of clause 1, wherein the data values are disposed in the electronic display proximal to the sensors from which the data values are output.
Clause 5. The system of clause 1, wherein the visual attributes further comprise an indication of a smart band set of sensors associated with the sensor from which the data values are output.
Clause 6. A system for data collection and visualization thereof in an industrial environment in which data values output by select sensors disposed in an augmented reality view of the industrial environment are displayed with visual attributes that indicate a degree of compliance of the data to an acceptable range or values for the sensed data.
Clause 7. The system of clause 6, wherein the sensors are selected based on a data collection template that facilitates configuring sensor data routing resources in the system.
Clause 8. The system of clause 7, wherein the select sensors are indicated in the template as part of a group of smart band sensors.
Clause 9. The system of clause 7, wherein the select sensors are sensors that are monitored for triggering a smart band data collection action.
Clause 10. The system of clause 6, wherein the select sensors are sensors that sense an aspect of the environment associated with a preventive maintenance criteria.
Clause 11. The system of clause 6, wherein the visual attributes further indicate if the acceptable range has been expanded or narrowed within the past 72 hours.
Clause 12. A system for data collection and visualization thereof in an industrial environment in which trends of data values output by select sensors disposed in a field of view of the industrial environment depicted in an augmented reality display are displayed with visual attributes that indicate a degree of severity of the trend.
Clause 13. The system of clause 12, wherein sensors are selected when data from the sensors exceed an acceptable range of values.
Clause 14. The system of clause 14, wherein sensors are selected based on the sensors being part of a smart band group of sensors.
Clause 15. The system of clause 12, wherein the visual attributes further indicate a compliance of the trend with an acceptable range of data values.
Clause 16. The system of clause 12, wherein the system for data collection is adapted to route data from the select sensors to a controller of the augmented reality display based on a data collection template that facilitates configuring routing resources of the system for data collection.
Clause 17. The system of clause 12, wherein the sensors are selected in response to the sensor data being configured in a smart band data collection template as an indication for triggering a smart band data collection action.
Clause 18. The system of clause 12, wherein the sensors are selected in response to a preventive maintenance criteria.
Clause 19. The system of clause 18, wherein the preventive maintenance criteria is selected from the list consisting of a preventive maintenance action is scheduled, a preventive maintenance action has been completed in the last 72 hours, a preventive maintenance action is overdue.
Disclosed herein are methods and systems for data collection in an industrial environment featuring self-organization functionality. Such data collection systems and methods may facilitate intelligent, situational, context-aware collection, summarization, storage, processing, transmitting, and/or organization of data, such as by one or more data collectors (such as any of the wide range of data collector embodiments described throughout this disclosure), a central headquarters or computing system, and the like. The described self-organization functionality of data collection in an industrial environment may improve various parameters of such data collection, as well as parameters of the processes, applications, and products that depend on data collection, such as data quality parameters, consistency parameters, efficiency parameters, comprehensiveness parameters, reliability parameters, effectiveness parameters, storage utilization parameters, yield parameters (including financial yield, output yield, and reduction of adverse events), energy consumption parameters, bandwidth utilization parameters, input/output speed parameters, redundancy parameters, security parameters, safety parameters, interference parameters, signal-to-noise parameters, statistical relevancy parameters, and others. The self-organization functionality may optimize across one or more such parameters, such as based on a weighting of the value of the parameters; for example, a swarm of data collectors may be managed (or manage itself) to provide a given level of redundancy for critical data, while not exceeding a specified level of energy usage, e.g., per data collector or a group of data collectors or the entire swarm of data collectors. This may include using a variety of optimization techniques described throughout this disclosure and the documents incorporated herein by reference.
In embodiments, such methods and systems for data collection in an industrial environment can include one or more data collectors, e.g., arranged in a cooperative group or “swarm” of data collectors, that collect and organize data in conjunction with a data pool in communication with a computing system, as well as supporting technology components, services, processes, modules, applications and interfaces, for managing the data collection (collectively referred to in some cases as a data collection system 12004). Examples of such components include, but are not limited to, a model-based expert system, a rule-based expert system, an expert system using artificial intelligence (such as a machine learning system, which may include a neural net expert system, a self-organizing map system, a human-supervised machine learning system, a state determination system, a classification system, or other artificial intelligence system), or various hybrids or combinations of any of the above. References to a self-organizing method or system should be understood to encompass utilization of any one of the foregoing or suitable combinations, except where context indicates otherwise.
The data collection systems and methods of the present disclosure can be utilized with various types of data, including but not limited to vibration data, noise data and other sensor data of the types described throughout this disclosure. Such data collection can be utilized for event detection, state detection, and the like, and such event detection, state detection, and the like can be utilized to self-organize the data collection systems and methods, as further discussed herein. The self-organization functionality may include managing data collector(s), both individually or in groups, where such functionality is directed at supporting an identified application, process, or workflow, such as confirming progress toward or/alignment with one or more objectives, goals, rules, policies, or guidelines. The self-organization functionality may also involve managing a different goal/guideline, or directing data collectors targeted to determining an unknown variable based on collection of other data (such as based on a model of the behavior of a system that involves the variable), selecting preferred sensor inputs among available inputs (including specifying combinations, fusions, or multiplexing of inputs), and/or specifying a specific data collector among available data collectors.
A data collector may include any number of items, such as sensors, input channels, data locations, data streams, data protocols, data extraction techniques, data transformation techniques, data loading techniques, data types, frequency of sampling, placement of sensors, static data points, metadata, fusion of data, multiplexing of data, self-organizing techniques, and the like as described herein. Data collector settings may describe the configuration and makeup of the data collector, such as by specifying the parameters that define the data collector. For example, data collector settings may include one or more frequencies to measure. Frequency data may further include at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope, as well as other signal characteristics described throughout this disclosure. Data collectors may include sensors measuring or data regarding one or more wavelengths, one or more spectra, and/or one or more types of data from various sensors and metadata Data collectors may include one or more sensors or types of sensors of a wide range of types, such as described throughout this disclosure and the documents incorporated by reference herein. Indeed, the sensors described herein may be used in any of the methods or systems described throughout this disclosure. For example, one sensor may be an accelerometer, such as one that measures voltage per G of acceleration (e.g., 100 mV/G, 500 mV/G, 1 V/G, 5 V/G, 10 V/G). In embodiments, a data collector may alter the makeup of the subset of the plurality of sensors used in a data collector based on optimizing the responsiveness of the sensor, such as for example choosing an accelerometer better suited for measuring acceleration of a lower speed gear system or drill/boring device versus one better suited for measuring acceleration of a higher speed turbine in a power generation environment. Choosing may be done intelligently, such as for example with a proximity probe and multiple accelerometers disposed on a specific target (e.g., a gear system, drill, or turbine) where while at low speed one accelerometer is used for measuring in the data collector and another is used at high speeds. Accelerometers come in various types, such as piezo-electric crystal, low frequency (e.g., 10V/G), high speed compressors (10 MV/G), MEMS, and the like. In another example, one sensor may be a proximity probe which can be used for sleeve or tilt-pad bearings (e.g., oil bath), or a velocity probe. In yet another example, one sensor may be a solid state relay (SSR) that is structured to automatically interface with another routed data collector (such as a mobile or portable data collector) to obtain or deliver data. In another example, a data collector may be routed to alter the makeup of the plurality of available sensors, such as by bringing an appropriate accelerometer to a point of sensing, such as on or near a component of a machine. In still another example, one sensor may be a triax probe (e.g., a 100 MV/G triax probe), that in embodiments is used for portable data collection. In some embodiments, of a triax probe, a vertical element on one axis of the probe may have a high frequency response while the ones mounted horizontally may influence limit the frequency response of the whole triax. In another example, one sensor may be a temperature sensor and may include a probe with a temperature sensor built inside, such as to obtain a bearing temperature. In still additional examples, sensors may be ultrasonic, microphone, touch, capacitive, vibration, acoustic, pressure, strain gauges, thermographic (e.g., camera), imaging (e.g., camera, laser, IR, structured light), afield detector, an EMF meter to measure an AC electromagnetic field, a gaussmeter, a motion detector, a chemical detector, a gas detector, a CBRNE detector, a vibration transducer, a magnetometer, positional, location-based, a velocity sensor, a displacement sensor, a tachometer, a flow sensor, a level sensor, a proximity sensor, a pH sensor, a hygrometer/moisture sensor, a densitometric sensor, an anemometer, a viscometer, or any analog industrial sensor and/or digital industrial sensor. In a further example, sensors may be directed at detecting or measuring ambient noise, such as a sound sensor or microphone, an ultrasound sensor, an acoustic wave sensor, and an optical vibration sensor (e.g., using a camera to see oscillations that produce noise). In still another example, one sensor may be a motion detector.
Data collectors may be of or may be configured to encompass one or more frequencies, wavelengths or spectra for particular sensors, for particular groups of sensors, or for combined signals from multiple sensors (such as involving multiplexing or sensor fusion). Data collectors may be of or may be configured to encompass one or more sensors or sensor data (including groups of sensors and combined signals) from one or more pieces of equipment/components, areas of an installation, disparate but interconnected areas of an installation (e.g., a machine assembly line and a boiler room used to power the line), or locations (e.g., a building in one geographic location and a building in a separate, different geographic location). Data collector settings, configurations, instructions, or specifications (collectively referred to herein using any one of those terms) may include where to place a sensor, how frequently to sample a data point or points, the granularity at which a sample is taken (e.g., a number of sampling points per fraction of a second), which sensor of a set of redundant sensors to sample, an average sampling protocol for redundant sensors, and any other aspect that would affect data acquisition.
Within the data collection system 12004, the self-organization functionality can be implemented by a neural net, a model-based system, a rule-based system, a machine learning system, and/or a hybrid of any of those systems. Further, the self-organizing functionality may be performed in whole or in part by individual data collectors, a collection or group of data collectors, a network-based computing system, a local computing system comprising one or more computing devices, a remote computing system comprising one or more computing devices, and a combination of one or more of these components. The self-organization functionality may be optimized for a particular goal or outcome, such as predicting and managing performance, health, or other characteristics of a piece of equipment, a component, or a system of equipment or components. Based on continuous or periodic analysis of sensor data, as patterns/trends are identified, or outliers appear, or a group of sensor readings begin to change, etc., the self-organization functionality may modify the collection of data intelligently, as described herein. This may occur by triggering a rule that reflects a model or understanding of system behavior (e.g., recognizing a shift in operating mode that calls for different sensors as velocity of a shaft increases) or it may occur under control of a neural net (either in combination with a rule-based approach or on its own), where inputs are provided such that the neural net over time learns to select appropriate collection modes based on feedback as to successful outcomes (e.g., successful classification of the state of a system, successful prediction, successful operation relative to a metric). For example only, when an assembly line is reconfigured for a new product or a new assembly line is installed in a manufacturing facility, data from the current data collector(s) may not accurately predict the state or metric of operation of the system, thus, the self-organization functionality may begin to iterate to determine if a new data collector, type of sensed data, format of sensed data, etc. is better at predicting a state or metric. Based on offset system data, such as from a library or other data structure, certain sensors, frequency bands or other data collectors may be used in the system initially and data may be collected to assess performance. As the self-organization functionality iterates, other sensors/frequency bands may be accessed to determine their relative weight in identifying performance metrics. Over time, a new frequency band may be identified (or a new collection of sensors, a new set of configurations for sensors, or the like) as a better or more suitable gauge of performance in the system and the self-organization functionality may modify its data collector(s) based on this iteration. For example only, perhaps an older boring tool in an energy extraction environment dampens one or more vibration frequencies while a different frequency is of higher amplitude and present during optimal performance than what was seen in the present system. In this example, the self-organization functionality may alter the data collectors from what was originally proposed, e.g., by the data collection system, to capture the higher amplitude frequency that is present in the current system.
The self-organization functionality, in embodiments involving a neural net or other machine learning system, may be seeded and may iterate, e.g., based on feedback and operation parameters, such as described herein. Certain feedback may include utilization measures, efficiency measures (e.g., power or energy utilization, use of storage, use of bandwidth, use of input/output use of perishable materials, use of fuel, and/or financial efficiency, financial such as reduction of costs), measures of success in prediction or anticipation of states (e.g., avoidance and mitigation of faults), productivity measures (e.g., workflow), yield measures, and profit measures. Certain parameters may include storage parameters (e.g., data storage, fuel storage, storage of inventory), network parameters (e.g., network bandwidth, input/output speeds, network utilization, network cost, network speed, network availability), transmission parameters (e.g., quality of transmission of data, speed of transmission of data, error rates in transmission, cost of transmission), security parameters (e.g., number and/or type of exposure events, vulnerability to attack, data loss, data breach, access parameters), location and positioning parameters (e.g., location of data collectors, location of workers, location of machines and equipment, location of inventory units, location of parts and materials, location of network access points, location of ingress and egress points, location of landing positions, location of sensor sets, location of network infrastructure, location of power sources), input selection parameters, data combination parameters (e.g., for multiplexing, extraction, transformation, loading), power parameters (e.g., of individual data collectors, groups of data collectors, or all potentially available data collectors), states (e.g., operational modes, availability states, environmental states, fault modes, health states, maintenance modes, anticipated states), events, and equipment specifications. With respect to states, operating modes may include, mobility modes (direction, speed, acceleration and the like), type of mobility modes (e.g., rolling, flying, sliding, levitation, hovering, floating), performance modes (e.g., gears, rotational speeds, heat levels, assembly line speeds, voltage levels, frequency levels), output modes, fuel conversion modes, resource consumption modes, and financial performance modes (e.g., yield, profitability). Availability states may refer to anticipating conditions that could cause machine to go offline or require backup. Environmental states may refer to ambient temperature, ambient humidity/moisture, ambient pressure, ambient wind/fluid flow, presence of pollution or contaminants, presence of interfering elements (e.g., electrical noise, vibration), power availability, and power quality, among other parameters. Anticipated states may include achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization, an avoidance of a fault condition (e.g., overheating, slow performance, excessive speed, excessive motion, excessive vibration/oscillation, excessive acceleration, expansion/contraction, electrical failure, running out of stored power/fuel, overpressure, excessive radiation/melt down, fire, freezing, failure of fluid flow (e.g., stuck valves, frozen fluids), mechanical failures (e.g., broken component, worn component, faulty coupling, misalignment, asymmetries/deflection, damaged component (e.g. deflection, strain, stress, cracking), imbalances, collisions, jammed elements, and lost or slipping chain or belt), avoidance of a dangerous condition or catastrophic failure, and availability (online status)).
The self-organization functionality may comprise or be seeded with a model that predicts an outcome or state given a set of data (which may comprise inputs from sensors, such as via a data collector, as well as other data, such as from system components, from external systems and from external data sources). For example, the model may be an operating model for an industrial environment, machine, or workflow. In another example, the model may be for anticipating states, for predicting fault and optimizing maintenance, for optimizing data transport (such as for optimizing network coding, network-condition-sensitive routing), for optimizing data marketplaces, and the like.
The self-organization functionality may result in any number of downstream actions based on analysis of data from the data collector(s). In an embodiment, the self-organization functionality may determine that the system should either keep or modify operational parameters, equipment or a weighting of a neural net model given a desired goal, such as a specified/threshold output production rate, specified/threshold generation rate, an operational efficiency/failure rate, a financial efficiency/profit goal, a power efficiency/resource utilization, an avoidance of a fault condition, an avoidance of a dangerous condition or catastrophic failure, and the like. In embodiments, the adjustments may be based on determining context of an industrial system, such as understanding a type of equipment, its purpose, its typical operating modes, the functional specifications for the equipment, the relationship of the equipment to other features of the environment (including any other systems that provide input to or take input from the equipment), the presence and role of operators (including humans and automated control systems), and ambient or environmental conditions. For example, in order to achieve a profit goal in a distribution environment (e.g., a power distribution environment), a generator or system of generators may need to operate at a certain efficiency level. The self-organization functionality may be seeded with a model for operation of the system of generators in a manner that results in a specified profit goal, such as indicating an on/off state for individual generator(s) in the power generation system based on the time of day, current market sale price for the fuel consumed by the generators, current demand or anticipated future demand, and the like. As it acquires data and iterates, the model predicts whether the profit goal will be achieved given the current data, and determine whether the data or type of data being collected is appropriate, sufficient, etc. for the model. Based on the results of the iteration, a recommendation may be made (or a control instruction may be automatically provided) to gather different/additional data, organize the data differently, direct different data collectors to collect new data, etc. and/or to operate a subset of the generators at a higher output (but less efficient) rate, power on additional generators, maintain a current operational state, or the like. Further, as the system iterates, one or more additional sensors may be sampled in the model to determine if their addition to the self-organization functionality would improve predicting a state or otherwise assisting with the goals of the data collection efforts.
In embodiments, a system for data collection in an industrial environment may include a plurality of input sensors, such as any of those described herein, communicatively coupled to a data collector having one or more processors. The data collection system may include a plurality of individual data collectors structured to operate together to determine at least one subset of the plurality of sensors from which to process output data. The data collection system may also include a machine learning circuit structured to receive output data from the at least one subset of the plurality of sensors and learn received output data patterns indicative of a state. In some embodiments, the data collection system may alter the at least one subset of the plurality of sensors, or an aspect thereof, based on one or more of the learned received output data patterns and the state. In certain embodiments, the machine learning circuit is seeded with a model that enables it to learn data patterns. The model may be a physical model, an operational model, a system model and the like. In other embodiments, the machine learning circuit is structured for deep learning wherein input data is fed to the circuit with no or minimal seeding and the machine learning data analysis circuit learns based on output feedback. For example, a metal tooling system in a manufacturing environment may operate to manufacture parts using machine tools such as lathes, milling machines, grinding machines, boring tools, and the like. Such machines may operate at various speeds and output rates, which may affect the longevity, efficiency, accuracy, etc. of the machine. The data collector may acquire various parameters to evaluate the environment of the machine tools, e.g., speed of operation, heat generation, vibration, and conformity with a part specification. The system can utilize such parameters and iterate towards a prediction of state, output rate, etc. based on such feedback. Further, the system may self-organize such that the data collector(s) collect additional/different data from which such predictions may be made.
There may be a balance of multiple goals/guidelines in the self-organization functionality of data collection system. For example, a repair and maintenance organization (RMO) may have operating parameters designed for maintenance of a machine in a manufacturing facility, while the owner of the facility may have particular operating parameters for the machine that are designed for meeting a production goal. These goals, in this example relating to a maintenance goal or a production output, may be tracked by a different data collectors or sensors. For example, maintenance of a machine may be tracked by sensors including a temperature sensor, a vibration transducer and a strain gauge while the production goal of a machine may be tracked by sensors including a speed sensor and a power consumption meter. The data collection system may (optionally using a neural net, machine learning system, deep learning system, or the like, which may occur under supervision by one or more supervisors (human or automated) intelligently manage data collectors aligned with different goals and assign weights, parameter modifications, or recommendations based on a factor, such as a bias towards one goal or a compromise to allow better alignment with all goals being tracked, for example. Compromises among the goals delivered to the data collection system may be based on one or more hierarchies or rules relating to the authority, role, criticality, or the like of the applicable goals. In embodiments, compromises among goals may be optimized using machine learning, such as a neural net, deep learning system, or other artificial intelligence system as described throughout this disclosure. For example, in a power plant where a turbine is operating, the data collection system may manage multiple data collectors, such as one directed to detecting the operational status of the turbine, one directed at identifying a probability of hitting a production goal, and one directed at determining if the operation of the turbine is meeting a fuel efficiency goal. Each of these data collectors may be populated with different sensors or data from different sensors (e.g., a vibration transducer to indicate operational status, a flow meter to indicate production goal, and a fuel gauge to indicate a fuel efficiency) whose output data are indicative of an aspect of a particular goal. Where a single sensor or a set of sensors is helpful for more than one goal, overlapping data collectors (having some sensors in common and other sensors not in common) may take input from that sensor or set of sensors, as managed by the data collection system. If there are constraints on data collection (such as due to power limitations, storage limitations, bandwidth limitations, input/output processing capabilities, or the like), a rule may indicate that one goal (e.g., a fuel utilization goal or a pollution reduction goal that is mandated by law or regulation) takes precedence, such that the data collection for the data collectors associated with that goal are maintained as others are paused or shut down. Management of prioritization of goals may be hierarchical or may occur by machine learning. The data collection system may be seeded with models, or may not be seeded at all, in iterating towards a predicted state (e.g., meeting a goal) given the current data it has acquired. In this example, during operation of the turbine the plant owner may decide to bias the system towards fuel efficiency. All of the data collectors may still be monitored, but as the self-organization functionality iterates and predicts that the system will not collect or is not collecting data sufficient to determine whether the system is or is not meeting a particular goal, the data collection system may recommended or implement changes directed at collecting the appropriate data. Further, the plant owner may structure the system with a bias towards a particular goal such that the recommended changes to data collection parameters affecting such goal are made in favor of making other recommended changes.
In embodiments, the data collection system may continue iterating in a deep-learning fashion to arrive at a distribution of data collectors, after being seeded with more than one data collection data type, that optimizes meeting more than one goal. For example, there may be multiple goals tracked for a refining environment, such as refining efficiency and economic efficiency. Refining efficiency for the refining system may be expressed by comparing fuel put into the system, which can be obtained by knowing the amount of and quality of the fuel being used, and the amount of the refined product output from the system, which is calculated using the flow out of the system. Economic efficiency of the refining system may be expressed as the ratio between costs to run the system, including fuel, labor, materials and services, and the refined product output from the system for a period of time. Data used to track refining efficiency may include data from a flow meter, quality data point(s), and a thermometer, and data used to track economic efficiency may be a flow of product output from the system and costs data. These data may be used in the data collection system to predict states, however, the self-organization functionality of the system may iterate towards a data collection strategy that is optimized to predict states related to both thermal and economic efficiency. The new data collection schema may include data used previously in the individual data collectors but may also use new data from different sensors or data sources.
The iteration of the data collection system may be governed by rules, in some embodiments. For example, the data collection system may be structured to collect data for seeding at a pre-determined frequency. The data collection system may be structured to iterate at least a number of times, such as when a new component/equipment/fuel source is added, when a sensor goes off-line, or as standard practice. For example, when a sensor measuring the rotation of a boring tool in an offshore drilling operation goes off-line and the data collection system begins acquiring data from a new sensor or data collector measuring the same data points, the data collection system may be structured to iterate for a number of times before the state is utilized in or allowed to affect any downstream actions. The data collection system may be structured to train off-line or train in situ/online. The data collection system may be structured to include static and/or manually input data in its data collectors. For example, a data collection system associated with such a boring tool may be structured to iterate towards predicting a distance bored based on a duration of operation, wherein the data collector(s) include data regarding the speed of the boring tools, a distance sensor, a temperature sensor, and the like.
In embodiments, the data collection system may be overruled. In embodiments, the data collection system may revert to prior settings, such as in the event the self-organization functionality fails, such as if the collected data is insufficient or inappropriately collected, if uncertainty is too high in a model-based system, if the system is unable to resolve conflicting rules in rule-based system, or the system cannot converge on a solution in any of the foregoing. For example, sensor data on a power generation system used by the data collection system may indicate a non-operational state (such as a seized turbine), but output sensors and visual inspection, such as by a drone, may indicate normal operation. In this event, the data collection system may revert to an original data collection schema for seeding the self-organization functionality. In another example, one or more point sensors on a refrigeration system may indicate imminent failure in a compressor, but the data collector self-organized to collect data associated towards determining a performance metric did not identify the failure. In this event, the data collector(s) will revert to an original setting or a version of the data collector setting that would have also identified the imminent failure of the compressor.
In embodiments, the data collection system may change data collector settings in the event that a new component is added that makes the system closer to a different system. For example, a vacuum distillation unit is added to an oil and gas refinery to distill naphthalene, but the current data collector settings for the data collection system are derived from a refinery that distills kerosene. In this example, a data structure with data collector settings for various systems may be searched for a system that is more closely matched to the current system. When a new system is identified as more closely matched, such as one that also distill naphthalene, the new data collector settings (which sensors to use, where to direct them, how frequently to sample, what types of data and points are needed, etc. as described herein) are used to seed the data collection system to iterate towards predicting a state for the system. In embodiments, the data collection system may change data collector settings in the event that a new set of data is available from a third party library. For example, a power generation plant may have optimized a specific turbine model to operate in a highly efficient way and deposited the data collector settings in a data structure. The data structure may be continuously scanned for new data collectors that better aid in monitoring power generation and thus, result in optimizing the operation of the turbine.
In embodiments, the data collection system may utilize self-organization functionality to uncover unknown variables. For example, the data collection system may iterate to identify a missing variable to be used for further iterations. For example, an under-utilized tank in a legacy condensate/make-up water system of a power station may have an unknown capacity because it is inaccessible and no documentation exists on the tank. Various aspects of the tank may be measured by a swarm of data collectors to arrive at an estimated volume (e.g., flow into a downstream space, duration of a dye traced solution to work through the system), which can then be fed into the data collection system as a new variable.
In embodiments, the data collection system node may be on a machine, on a data collector (or a group of them), in a network infrastructure (enterprise or other), or in the cloud. In embodiments, there may be distributed neurons across nodes (e.g., machine, data collector, network, cloud).
In an aspect, and as illustrated in
The targets 12002 can be any form of machinery or component thereof in an industrial environment 12000. Examples of such industrial environments 12000 include but are not limited to factories, pipelines, construction sites, ocean oil rigs, ships, airplanes or other aircraft, mining environments, drilling environments, refineries, distribution environments, manufacturing environments, energy source extraction environments, offshore exploration sites, underwater exploration sites, assembly lines, warehouses, power generation environments, and hazardous waste environments, each of which may include one or more targets 12002. Targets 12002 can take any form of item or location at which a sensor can obtain data. Examples of such targets 12002 include but are not limited to machines, pipelines, equipment, installations, tools, vehicles, turbines, speakers, lasers, automatons, computer equipment, industrial equipment, and switches.
The self-organization functionality of the data collection system 12004 can be performed at or by any of the components of the data collection system 12004. In embodiments, a data collector 12008 or the swarm 12006 of data collectors 12008 can self-organize without assistance from other components and based on, e.g., the data sensed by its associated sensors and other knowledge. In embodiments, the network 12010 can self-organize without assistance from other components and based on, e.g., the data sensed by the data collectors 12008 or other knowledge. Similarly, the computing system 12012 and/or the data pool 12014 without assistance from other components and based on, e.g., the data sensed by the data collectors 12008 or other knowledge. It should be appreciated that any combination or hybrid-type self-organization system can also be implemented.
For example only, the data collection system 12004 can perform or enable various methods or systems for data collection having self-organization functionality in an industrial environment 12000. These methods and systems can include analyzing a plurality of sensor inputs, e.g., received from or sensed by sensors at the data collector(s) 12008. The methods and systems can also include sampling the received data and self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs.
In aspects, the storage operation can include storing the data in a local database, e.g., of a data collector 12008, a computing system 12012, and/or a data pool 12014. The data can also be summarized over a given time period to reduce a size of the sensed data. The summarized data can be sent to one or more data acquisition boxes, to one or more data centers, and/or to other components of the system or other, separate systems Summarizing the data over a given time period to reduce the size of the data, in some aspects, can include determining a speed at which data can be sent via a network (e.g., network 12010), wherein the size of the summarized data corresponds to the speed at which data can be sent continuously in real time via the network. In such aspects, or others, the summarized data can be continuously sent, e.g., to an external device via the network.
In various implementations, the methods and systems can include committing the summarized data to a local ledger, identifying one or more other accessible signal acquisition instruments on an accessible network, and/or synchronizing the summarized data at the local ledger with at least one of the other accessible signal acquisition instruments (e.g., data collectors 12008). In embodiments, receiving a remote stream of sensor data from one or more other accessible signal acquisition instruments via a network can be included. An advertisement message to a potential client indicating availability of at least one of the locally stored data, the summarized data, and the remote stream of sensor data can also or alternatively be sent.
The methods and systems can include identifying one or more other accessible signal acquisition instruments (e.g., data collectors 12008) on an accessible network (e.g., 12010), nominating at least one of the one or more other accessible signal acquisition instruments as a logical communication hub, and providing the logical communication hub with a list of available data and their associated sources. The list of available data and their associated sources can be provided to the logical communication hub utilizing a hybrid peer-to-peer communications protocol.
In some aspects, the storage operation can include storing the data in a local database and automatically organizing at least one parameter of the data pool utilizing machine learning. The organizing can be based at least in part on receiving information regarding at least one of an accuracy of classification and an accuracy of prediction of an external machine learning system that uses data from the data pool (e.g., data pool 12014).
1. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs.
2. A system for data collection in an industrial environment having automated self-organization, comprising:
a data collector for handling a plurality of sensor inputs from sensors in the industrial environment and for generating data associated with the plurality of sensor inputs; and
a self-organizing system for self-organizing at least one of (i) a storage operation of the data; (ii) a data collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs.
3. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs,
wherein the storage operation comprises:
storing the data in a local database, and
summarizing the data over a given time period to reduce a size of the data.
4. The method of clause 3, further comprising sending the summarized data to one or more data acquisition boxes.
5. The method of clause 3, further comprising sending the summarized data to one or more data centers.
6. The method of clause 3, wherein summarizing the data over a given time period to reduce the size of the data comprises determining a speed at which data can be sent via a network, wherein the size of the summarized data corresponds to the speed at which data can be sent continuously in real time via the network.
7. A method of, further comprising continuously sending the summarized data to an external device via the network.
8. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs,
wherein the storage operation comprises:
storing the data in a local database,
summarizing the data over a given time period to reduce a size of the data,
committing the summarized data to a local ledger;
identifying one or more other accessible signal acquisition instruments on an accessible network; and
synchronizing the summarized data at the local ledger with at least one of the other accessible signal acquisition instruments.
9. The method of clause 3, further comprising:
receiving a remote stream of sensor data from one or more other accessible signal acquisition instruments via a network.
10. The method of clause 3, further comprising sending an advertisement message to a potential client indicating availability of at least one of the locally stored data, the summarized data, and the remote stream of sensor data.
11. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs;
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs,
wherein the storage operation comprises:
storing the data in a local database, and
summarizing the data over a given time period to reduce a size of the data;
identifying one or more other accessible signal acquisition instruments on an accessible network;
nominating at least one of the one or more other accessible signal acquisition instruments as a logical communication hub; and
providing the logical communication hub with a list of available data and their associated sources.
12. The method of clause 11, wherein the list of available data and their associated sources is provided to the logical communication hub utilizing a hybrid peer-to-peer communications protocol.
13. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs,
wherein the storage operation comprises:
storing the data in a local database,
summarizing the data over a given time period to reduce a size of the data,
storing the data in a local database, and
automatically organizing at least one parameter of the database utilizing machine learning, wherein the organizing is based at least in part on receiving information regarding at least one of an accuracy of classification and an accuracy of prediction of an external machine learning system that uses data from the database.
In aspects, the collection operation of sensors that provide the plurality of sensor inputs can include receiving instructions directing a mobile data collector unit (e.g., data collector 12008) to operate sensors at a target (e.g., 12002), wherein at least one of the plurality of sensors is arranged in the mobile data collector unit. A communication can be transmitted to one or more other mobile data collector units (12008) regarding the instructions. The swarm 12006 or portion thereof can self-organize a distribution (the swarm 12006) of the mobile data collector unit and the one or more other mobile data collector units (e.g., data collectors 12008) at the target 12002.
In aspects, self-organizing the distribution of the mobile data collector units at the target 12002 comprises utilizing a machine learning algorithm to determine a respective target location for each of the mobile data collector units. The machine learning algorithm can utilize one or more of a plurality of features to determine the respective target locations. Examples of the features can include: battery life of the mobile data collector units (data collectors 12008), a type of the target 12002 being sensed, a type of signal being sensed, a size of the target 12002, a number of mobile data collector units (data collectors 12008) needed to cover the target 12002, a number of data points needed for the target 12002, a success in prior accomplishment of signal capture, information received from a headquarters or other components from which the instructions are received, and historical information regarding the sensors operated at the target 12002.
In implementations, self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location can include proposing a target location for the mobile data collector unit(s), transmitting the target location to at least one other mobile data collector units, receiving confirmation that there is no contention for the target location, directing one of the mobile data collector units to the target location, and collecting sensor data at the target location from the directed mobile data collector unit.
Self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location can also include, in certain embodiments, proposing a target location for the mobile data collector unit, transmitting the target location to at least one of the one or more other mobile data collector units, receiving a proposal for a new target location, directing the mobile data collector unit to the new target location, and collecting sensor data at the new target location from the mobile data collector unit.
In additional or alternative aspects, self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location can comprise proposing a target location for the mobile data collector unit, determining that at least one of the one or more other mobile data collector units is at or moving to the target location, determining a new target location based on the at least one of the one or more other mobile data collector units being at or moving to the target location, directing the mobile data collector unit to the new target location, and collecting sensor data at the new target location from the mobile data collector unit.
Self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location can further comprise determining a type of the sensors to operate at the target 12002, receiving confirmation that there is no contention for the type of sensors, directing the mobile data collector unit to operate the type of sensors at the target 12002, and collecting sensor data from the type of sensors at the target 12002 from the mobile data collector unit.
In aspects, self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location can include determining a type of the sensors to operate at the target, transmitting the type of the sensors to at least one of the one or more other mobile data collector units, receiving a proposal for a new type of the sensors, directing the mobile data collector unit to operate the new type of sensors at the target, and collecting sensor data from the new type of sensors at the target from the mobile data collector unit.
Self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location can include determining a type of the sensors to operate at the target, determining that at least one of the one or more other mobile data collector units is operating or can operate the type of the sensors at the target, determining a new type of the sensors based on the at least one of the one or more other mobile data collector units operating or being capable of operating the type of the sensors at the target, directing the mobile data collector unit to operate the new type of sensors at the target, and collecting sensor data from the new type of sensors at the target from the mobile data collector unit.
Self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location, in some implementations, can comprise utilizing a swarm optimization algorithm to allocate areas of sensor responsibility amongst the mobile data collector unit and the one or more other mobile data collector units. Examples of the swarm optimization algorithm include but are not limited to Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Glowworm Swarm Optimization (GSO), and Cuckoo Search Algorithm (CSA), Genetic Programming (GP), Evolution Strategy (ES), Evolutionary Programming (EP), Firefly Algorithm (FA), Bat Algorithm (BA) and Grey Wolf Optimizer (GWO), or combinations thereof.
1. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs.
2. A system for data collection in an industrial environment having automated self-organization, comprising:
a data collector for handling a plurality of sensor inputs from sensors in the industrial environment and for generating data associated with the plurality of sensor inputs; and
a self-organizing system for self-organizing at least one of (i) a storage operation of the data; (ii) a data collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs.
3. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs,
wherein the collection operation of sensors that provide the plurality of sensor inputs comprises:
receiving instructions directing a mobile data collector unit to operate sensors at a target, wherein at least one of the plurality of sensors is arranged in the mobile data collector unit,
transmitting a communication to one or more other mobile data collector units regarding the instructions, and
self-organizing a distribution of the mobile data collector unit and the one or more other mobile data collector units at the target.
4. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target comprises utilizing a machine learning algorithm to determine a respective target location for each of the mobile data collector units.
5. The method of clause 4, wherein the machine learning algorithm utilizes one or more of a plurality of features to determine the respective target locations, the plurality of features including: battery life of the mobile data collector units, a type of the target being sensed, a type of signal being sensed, a size of the target, a number of mobile data collector units needed to cover the target, a number of data points needed for the target, a success in prior accomplishment of signal capture, information received from a headquarters from which the instructions are received, and historical information regarding the sensors operated at the target.
6. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises:
proposing a target location for the mobile data collector unit;
transmitting the target location to at least one of the one or more other mobile data collector units;
receiving confirmation that there is no contention for the target location;
directing the mobile data collector unit to the target location; and
collecting sensor data at the target location from the mobile data collector unit.
7. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises:
proposing a target location for the mobile data collector unit;
transmitting the target location to at least one of the one or more other mobile data collector units;
receiving a proposal for a new target location;
directing the mobile data collector unit to the new target location; and
collecting sensor data at the new target location from the mobile data collector unit.
8. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises:
proposing a target location for the mobile data collector unit;
determining that at least one of the one or more other mobile data collector units is at or moving to the target location;
determining a new target location based on the at least one of the one or more other mobile data collector units being at or moving to the target location;
directing the mobile data collector unit to the new target location; and
collecting sensor data at the new target location from the mobile data collector unit.
9. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises:
determining a type of the sensors to operate at the target;
receiving confirmation that there is no contention for the type of sensors;
directing the mobile data collector unit to operate the type of sensors at the target; and
collecting sensor data from the type of sensors at the target from the mobile data collector unit.
10. A method for data collection in an industrial environment having self-organization functionality, comprising:
analyzing a plurality of sensor inputs;
sampling data received from the sensor inputs; and
self-organizing at least one of: (i) a storage operation of the data; (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs,
wherein the collection operation of sensors that provide the plurality of sensor inputs comprises:
receiving instructions directing a mobile data collector unit to operate sensors at a target, wherein at least one of the plurality of sensors is arranged in the mobile data collector unit,
transmitting a communication to one or more other mobile data collector units regarding the instructions,
self-organizing a distribution of the mobile data collector unit and the one or more other mobile data collector units at the target, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises:
determining a type of the sensors to operate at the target;
transmitting the type of the sensors to at least one of the one or more other mobile data collector units;
receiving a proposal for a new type of the sensors;
directing the mobile data collector unit to operate the new type of sensors at the target; and
collecting sensor data from the new type of sensors at the target from the mobile data collector unit.
11. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises:
determining a type of the sensors to operate at the target;
determining that at least one of the one or more other mobile data collector units is operating or can operate the type of the sensors at the target;
determining a new type of the sensors based on the at least one of the one or more other mobile data collector units operating or being capable of operating the type of the sensors at the target;
directing the mobile data collector unit to operate the new type of sensors at the target; and
collecting sensor data from the new type of sensors at the target from the mobile data collector unit.
12. The method of clause 3, wherein self-organizing the distribution of the mobile data collector unit and the one or more other mobile data collector units at the target location comprises utilizing a swarm optimization algorithm to allocate areas of sensor responsibility amongst the mobile data collector unit and the one or more other mobile data collector units.
13. The method of clause 12, wherein the swarm optimization algorithm is one or more types of Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Glowworm Swarm Optimization (GSO), and Cuckoo Search Algorithm (CSA), Genetic Programming (GP), Evolution Strategy (ES), Evolutionary Programming (EP), Firefly Algorithm (FA), Bat Algorithm (BA) and Grey Wolf Optimizer (GWO).
Referencing
In certain embodiments, and as illustrated in
The example system 12200 further includes a mesh network 12220 having a plurality of network nodes depicted thereupon. The mesh network 12220 is depicted in a single location for convenience of illustration, but it will be understood that any network infrastructure that is within the system 12200, and/or within communication with the system 12200, including intermittently, is contemplated within the system network. Additionally, any or all of the cloud server 12214, plant computer 12210, controller 12212, data controller 12208, any network capable sensor 12206, and/or user associated device 12218 may be a part of the network for the system, including a mesh network 12220, during at least certain operating conditions of the system 12200. Additionally, or alternatively, the system 12200 may utilize a hierarchical network, a peer-to-peer network, a peer-to-peer network with one or more super-nodes, combinations of these, hybrids of these, and/or may include multiple networks within the system 12200 or in communication with the system. It will be appreciated that certain features and operations of the present disclosure are beneficial to only one or more than one of these types of networks, certain features and operations of the present disclosure are beneficial to any type of network, and certain features and operations are particularly beneficial to combinations of these networks, and/or to networks having multiple networking options within the network, where the benefits relate to the utilization of options of any type, or where the benefits relate to one or more options being of a specific network type.
Referencing
The example controller 12212 includes a transmission environment circuit 12226 that determines transmission conditions 12254 corresponding to the communication of the at least a portion of the number of sensor data values to the storage target computing device 12252. Transmission conditions 12254 include any conditions affecting the transmission of the data. For example, referencing
Referencing
An example transmission condition 12254 includes a node in a mesh or hierarchical network detected as malicious (e.g., from another supervisory process, heuristically, or as indicated to the system 12200); a peer node has experienced a bandwidth or connectivity change 12296 (e.g., mesh network peer that was forwarding packets has lost connectivity, gained additional bandwidth, had a reduction in available bandwidth, and/or has regained connectivity). An example transmission condition 12254 includes a change in a cost of transmitting information 12298 (e.g., cost has increased or decreased, where cost may be a direct cost parameter such as a data transmission subscription cost, or an abstracted cost parameter reflecting overall system priorities, and/or a current cost of delivering information over a network hop has changed), a change has been made in a hierarchical network arrangement (e.g., network arrangement change 12300) such as to balance bandwidth use in a network tree; and/or a change in a permission scheme 12302 (e.g., a portion of the network relaying sampling data has had a change in permissions, authorization level, or credentials). Certain further example transmission conditions 12254 include the availability of an additional connection type 12304 (e.g., a higher-bandwidth network connection type has become available, and/or a lower-cost network connection type has become available); a change has been made in a network topology 12306 (e.g., a node has gone offline or online, a mesh change has occurred, and/or a hierarchy change has occurred); and/or a data collection client changed a preference or a requirement 12308 (e.g., a data frequency requirement for at least one of the number of sensor values; a data type requirement for at least one of the number of sensor values; a sensor target for data collection; and/or a data collection client has changed the storage target computing device, which may change the network delivery outcomes and routing).
The example controller 12212 includes a network management circuit 12230 that updates the sensor data transmission protocol 12232 in response to the transmission conditions 12254. For example, where the transmission conditions 12254 indicate that a current routing, protocol, delivery frequency, delivery rate, and/or any other parameter associated with communicating the sensor data values 12244 is no longer cost effective, possible, optimal, and/or where an improvement is available, the network management circuit 12230 updates the sensor data transmission protocol 12232 in response—to a lower cost, possible, optimal, and/or improved transmission condition. The example system collaboration circuit 12228 is further responsive to the updated sensor data transmission protocol 12232—for example implementing subsequent communications of the sensor data values 12244 in compliance with the updated sensor data transmission protocol 12232, providing a communication to the network management circuit 12230 indicating which aspects of the updated sensor data transmission protocol 12232 cannot be or are not being followed, and/or providing an alert (e.g., to an operator, a network node, controller 12212, and/or the network management circuit 12230) indicating that a change is requested, indicating that a change is being implemented, and/or indicating that a requested change cannot be or is not being implemented.
An example system 12200 includes the transmission conditions 12254 being environmental conditions 12272 relating to sensor communication of the number of sensor data values 12244, where the network management circuit 12230 further analyzes the environmental conditions 12272, and where updating the sensor data transmission protocol 12232 includes modifying the manner in which the number of sensor data values are transmitted from the number of sensors 12206 to the storage target computing device. An example system further includes a data collector 12208 communicatively coupled to at least a portion of the number of sensors 12206 and responsive to the sensor data transmission protocol 12232, where the system collaboration circuit 12228 further receives the number of sensor data values 12244 from the at least a portion of the number of sensors, and where the transmission conditions 12254 correspond to at least one network parameter corresponding to the communication of the number of sensor data values from the at least a portion of the number of sensors. Referencing
An example network management circuit 12230 further updates the sensor data transmission protocol 12232 to adjust a network transmission parameter (e.g., any network parameter 12276) for at least a portion of the number of sensor values. For example, certain network parameters that are not control variables and/or are not currently being controlled are transmission conditions 12254, and certain network parameters are control variables and subject to change in response to the data transmission protocol 12232, and/or the network management circuit 12230 can optionally take control of certain network parameters to make them control variables. An example network management circuit 12230 further updates the sensor data transmission protocol 12232 to change any one or more of: a frequency of data transmitted; a quantity of data transmitted; a destination of data transmitted (including a target or intermediate destination, and/or a routing); a network protocol used to transmit the data; and/or a network path (e.g., providing a redundant path to transmit the data (e.g., where high noise, high network loss, and/or critical data are involved, the network management circuit 208 may determine that the system operations are improved with redundant pathing for some of the data)). An example network management circuit 12230 further updates the sensor data transmission protocol 12232, such as to: bond an additional network path to transmit the data (e.g., the network management circuit 208 may have authority to bring additional network resources online, and/or selectively access additional network resources); re-arrange a hierarchical network to transmit the data (e.g., add or remove a hierarchy layer, change a parent-child relationship, etc.—for example to provide critical data with additional paths, fewer layers, and/or a higher priority path); rebalance a hierarchical network to transmit the data; and/or reconfigure a mesh network to transmit the data. An example network management circuit 12230 further updates the sensor data transmission protocol 12232 to delay a data transmission time, and/or delay the data transmission time to a lower cost transmission time.
An example network management circuit further updates the sensor data transmission protocol 12232 to reduce the amount of information sent at one time over the network and/or updates the sensor data transmission protocol to adjust a frequency of data sent from a second data collector (e.g., an offset data collector within or not within the direct purview of the network management circuit 12230, but where network resource utilization from the second data collector competes with utilization of the first data collector).
An example network management circuit 12230 further adjusts an external data access frequency 12234—for example where the expert system 12242 and/or the machine learning algorithm 12248 access external data 12246 to make continuous improvements to the system (e.g., accessing information outside of the sensor data values 12244, and/or from offset systems or aggregated cloud based data), and/or an external data access timing value (12236). The control of external data 12246 access allows for control of network utilization when the system is low on resources, when high fidelity and/or frequency of sensor data values 12244 is prioritized, and/or shifting of resource utilization into lower cost portions of the operating space of the system. In certain embodiments, the system collaboration circuit 12228 accesses the external data 12246, and is responsive to the adjusted external data access frequency 12234 and/or external data access timing value 12236. An example network management circuit 12230 further adjusts a network utilization value 12238—for example to keep system utilization operations below a threshold to reserve margin and/or to avoid the need for capital cost upgrades to the system due to capacity limitations. An example network management circuit 12230 adjusts the network utilization value 12238 to utilize bandwidth at a lower cost bandwidth time—for example when competing traffic is lower, when network utilization does not adversely affect other system processes, and/or when power consumption costs are lower.
An example network management circuit further 12230 enables utilizing a high-speed network, and/or requests a higher cost bandwidth access—for example when system process improvements are sufficient that higher costs are justified, to meet a minimum delivery requirement for data, and/or to move aging data from the system before it becomes obsolete or must be deleted to make room for subsequent data.
An example network management circuit 12230 further includes an expert system 12242, where the updating the sensor data transmission protocol 12232 is further in response to operations of the expert system 12242. The self-organized, network-sensitive data collection system may manage or optimize any such parameters or factors noted throughout this disclosure, individually or in combination, using an expert system, which may involve a rule-based optimization, optimization based on a model of performance, and/or optimization using machine learning/artificial intelligence, optionally including deep learning approaches, or a hybrid or combination of the above. Referencing
An example network management circuit 12230 further includes a machine learning algorithm 12248, where updating the sensor data transmission protocol 12232 is further in response to operations of the machine learning algorithm 12248. An example machine learning algorithm 12248 utilizes a machine learning optimization routine, and upon determining that an improved sensor data transmission protocol 12232 is available, the network management circuit 12230 provides the updated sensor data transmission protocol 12232 which is utilized by the system collaboration circuit 12228. In certain embodiments, the network management circuit 12230 may perform various operations such as supplying an sensor data transmission protocol 12232 which is utilized by the system collaboration circuit 12228 to produce real-world results, applies modeling to the system (either first principles modeling based on system characteristics, a model utilizing actual operating data for the system, a model utilizing actual operating data for an offset system, and/or combinations of these) to determine what an outcome of a given sensor data transmission protocol 12232 will be or would have been (including, for example, taking extra sensor data beyond what is utilized to support a process operated by the system, and/or utilizing external data 12246 and/or benchmarking data 12240), and/or applying randomized changes to the sensor data transmission protocol 12232 to ensure that an optimization routine does not settle into a local optimum or non-optimal condition.
An example machine learning algorithm 12248 further utilizes feedback data including the transmission conditions 12254, at least a portion of the number of sensor data values 12244; and/or where the feedback data includes benchmarking data 12240. Referencing
Referencing
Yet another example system includes an industrial system including a number of components, and a number of sensors each operatively coupled to at least one of the number of components; a sensor communication circuit that interprets a number of sensor data values from the number of sensors; a system collaboration circuit that communicates at least a portion of the number of sensor data values over a network having a number of nodes to a storage target computing device according to a sensor data transmission protocol; a transmission environment circuit that determines transmission feedback corresponding to the communication of the at least a portion of the number of sensor data values over the network; and a network management circuit updates the sensor data transmission protocol in response to the transmission feedback. The example system collaboration circuit is further responsive to the updated sensor data transmission protocol.
Referencing
In certain embodiments, updating the sensor data transmission protocol 12232 includes the network management circuit 12230 providing instructions to reduce a quantity of data sent over the network; providing instructions to adjust a frequency of data capture sent over the network; providing instructions to time-shift delivery of at least a portion of the number of sensor values sent over the network (e.g., utilizing intermediate storage); providing instructions to change a network protocol corresponding to the network; providing instructions to reduce a throughput of at least one device coupled to the network; providing instructions to reduce a bandwidth use of the network; providing instructions to compress data corresponding to at least a portion of the number of sensor values sent over the network; providing instructions to condense data corresponding to at least a portion of the number of sensor values sent over the network (e.g., providing a relevant subset, reduced sample rate data, etc.); providing instructions to summarize data (e.g., providing a statistical description, an aggregated value, etc.) corresponding to at least a portion of the number of sensor values sent over the network; providing instructions to encrypt data corresponding to at least a portion of the number of sensor values sent over the network (e.g., to enable using an alternate, less secure network path, and/or to access another network path requiring encryption); providing instructions to deliver data corresponding to at least a portion of the number of sensor values to a distributed ledger; providing instructions to deliver data corresponding to at least a portion of the number of sensor values to a central server (e.g., the plant computer 12212 and/or cloud server 12214); providing instructions to deliver data corresponding to at least a portion of the number of sensor values to a super-node; and providing instructions to deliver data corresponding to at least a portion of the number of sensor values redundantly across a number of network connections. In certain embodiments, updating the sensor data transmission includes providing instructions to deliver data corresponding to at least a portion of the number of sensor values to one of the components (e.g., where one or more components 12204 in the system has memory storage and is communicatively accessible to the sensor 12206, the data collector 12208, and/or the network), and/or where the one of the components is communicatively coupled to the sensor providing the data corresponding to at least a portion of the number of sensor values (e.g., where the data to be stored on the component 12204 is the component the data was measured for, or is in proximity to the sensor 12206 taking the data).
An example network includes a mesh network, and where the network management circuit 12230 further updates the sensor data transmission protocol 12232 to provide instructions to eject (e.g., remove from the mesh map, take it out of service, etc.) one of the number of nodes from the mesh network. An example network includes a peer-to-peer network, where the network management circuit 12230 further updates the sensor data transmission protocol 12232 to provide instructions to eject one of the number of nodes from the peer-to-peer network.
An example network management circuit 12230 further updates the sensor data transmission protocol 12232 to cache (e.g., as a sensor data cache 12260) at least a portion of the number of sensor data values 12244. In certain further embodiments, the network management circuit 12230 further updates the sensor data transmission protocol 12232 to communicate the cached sensor values 12260 in response to at least one of: a determination that the cached data is requested (e.g., a user, model, machine learning algorithm, expert system, etc. has requested the data); a determination that the network feedback indicates communication of the cached data is available (e.g., a prior limitation on the network leading the network management circuit 12230 to direct caching is now lifted or improved); and/or a determination that higher priority data is present that requires utilization of cache resources holding the cached data 12260.
An example system 12200 for self-organized, network-sensitive data collection in an industrial environment includes an industrial system 12202 having a number of components 12204 and a number of sensors 12206 each operatively coupled to at least one of the number of components 12204. A sensor communication circuit 12224 interprets the number of sensor data values 12244 from the number of sensors at a predetermined frequency. The system collaboration circuit 12228 that communicates at least a portion of the number of sensor data values 12244 over a network having a number of nodes to a storage target computing device according to the sensor data transmission protocol 12232, where the sensor data transmission protocol 12232 includes a predetermined hierarchy of data collection and the predetermined frequency. An example data management circuit 12230 adjusts the predetermined frequency in response to transmission conditions 12254, and/or in response to benchmarking data 12240.
Referring to
An example transmission feedback includes a feedback value such as: a change in transmission pricing, a change in storage pricing, a loss of connectivity, a reduction of bandwidth, a change in connectivity, a change in network availability, a change in network range, a change in wide area network (WAN) connectivity, and/or a change in wireless local area network (WLAN) connectivity.
An example system includes an assembly line industrial system having a number of vibrating components, such as motors, conveyors, fans, and/or compressors. The system includes a number of sensors that determine various parameters related to the vibrating components, including determination of diagnostic and/or process related information (proper operation, off-nominal operation, operating speed, imminent servicing or failure, etc.) of one or more of the components. Example sensors, without limitation, include noise, vibration, acceleration, temperature, and/or shaft speed sensors. The sensor information is conveyed to a target storage system, including at least partially through a network communicatively coupled to the assembly line industrial system. The example system includes a network management circuit that determines a sensor data transmission protocol to control flow of data from the sensors to the target storage system. The network management circuit, a related expert system, and/or a related machine learning algorithm, updates the sensor data transmission protocol to ensure efficient network utilization, sufficient delivery of data to support system control, diagnostics, and/or other determinations planned for the data outside of the system, to reduce resource utilization of data transmission, and/or to respond to system noise factors, variability, and/or changes in the system or related aspects such as cost or environment parameters. The example system includes improvement of system operations to ensure that diagnostics, controls, or other data dependent operations can be completed, to reduce costs while maintaining performance, and/or to increase system capability over time or process cycles.
An example system includes an automated robotic handling system, including a number of components such as actuators, gear boxes, and/or rail guides. The system includes a number of sensors that determine various parameters related to the components, including without limitation actuator position and/or feedback sensors, vibration, acceleration, temperature, imaging sensors, and/or spatial position sensors (e.g., within the handling system, a related plant, and/or GPS-type positioning). The sensor information is conveyed to a target storage system, including at least partially through a network communicatively coupled to the automated robotic handling system. The example system includes a network management circuit that determines a sensor data transmission protocol to control flow of data from the sensors to the target storage system. The network management circuit, a related expert system, and/or a related machine learning algorithm, updates the sensor data transmission protocol to ensure efficient network utilization, sufficient delivery of data to support system control, diagnostics, improvement and/or efficiency updates to handling efficiency, and/or other determinations planned for the data outside of the system, to reduce resource utilization of data transmission, and/or to respond to system noise factors, variability, and/or changes in the system or related aspects such as cost or environment parameters. The example system includes improvement of system operations to ensure that diagnostics, controls, or other data dependent operations can be completed, to reduce costs while maintaining performance, and/or to increase system capability over time or process cycles.
An example system includes a mining operation, including a surface and/or underground mining operation. The example mining operation includes components such as an underground inspection system, pumps, ventilation, generators and/or power generation, gas composition or quality systems, and/or process stream composition systems (e.g., including determination of desired material compositions, and/or composition of effluent streams for pollution and/or regulatory control). Various sensors are present in an example system to support control of the operation, determine status of the components, support safe operation, and/or to support regulatory compliance. The sensor information is conveyed to a target storage system, including at least partially through a network communicatively coupled to the mining operation. In certain embodiments, the network infrastructure of the mining operation exhibits high variability, due to, without limitation, significant environmental variability (e.g., pit or shaft condition variability) and/or intermittent availability—e.g. shutting off electronics during certain mining operations, difficulty in providing network access to portions of the mining operation, and/or the desirability to include mobile or intermittently available devices within the network infrastructure. The example system includes a network management circuit that determines a sensor data transmission protocol to control flow of data from the sensors to the target storage system. The network management circuit, a related expert system, and/or a related machine learning algorithm, updates the sensor data transmission protocol to ensure efficient network utilization, sufficient delivery of data to support system control, diagnostics, improvement and/or efficiency updates to handling efficiency, support for financial and/or regulatory compliance, and/or other determinations planned for the data outside of the system, to reduce resource utilization of data transmission, and/or to respond to system noise factors, variability, network infrastructure challenges, and/or changes in the system or related aspects such as cost or environment parameters.
An example system includes an aerospace system, such as a plane, helicopter, satellite, space vehicle or launcher, orbital platform, and/or missile. Aerospace systems have numerous systems supported by sensors, such as engine operations, control surface status and vibrations, environmental status (internal and external), and telemetry support. Additionally, aerospace systems have high variability in both the number of sensors of varying types (e.g., a small number of fuel pressure sensors, but a large number of control surface sensors) as well as the sampling rates for relevant determinations of sensors of varying types (e.g., 1-second data may be sufficient for internal cabin pressure, but weather radar or engine speed sensors may require much higher time resolution). Computing power on an aerospace application is at a premium due to power consumption and weight considerations, and accordingly iterative, recursive, deep learning, expert system, and/or machine learning operations to improve any systems on the aerospace system, including sensor data taking and transmission of sensor information, are driven in many embodiments to computing devices outside of the aerospace vehicle of the system (e.g., through offline learning, post-processing, or the like). Storage capacity on an aerospace application is similarly at a premium, such that long-term storage of sensor data on the aerospace vehicle is not a cost-effective solution for many embodiments. Additionally, network communication from an aerospace vehicle may be subject to high variability and/or bandwidth limitations as the vehicle moves rapidly through the environment and/or into areas where direct communication with ground-based resources is not practical. Further, certain aerospace applications have significant competition for available network resources—for example in environments with a large number of passengers where passenger utilization of a network infrastructure consumes significant bandwidth. Accordingly, it can be seen that operations of a network management circuit, a related expert system, and/or a related machine learning algorithm, to update the sensor data transmission protocol can significantly enhance sensing operations in various aerospace systems. Additionally, certain aerospace applications have a high number of offset systems, enhancing the ability of an expert system or machine learning algorithm to improve sensor data capture and transmission operations, and/or to manage the high variability in sensed parameters (frequency, data rate, and/or data resolution) for the system across operating conditions.
An example system includes an oil or gas production system, such as a production platform (onshore or offshore), pumps, rigs, drilling equipment, blenders, and the like. Oil and gas production systems exhibit high variability in sensed variable types and sensing parameters, such as vibration (e.g., pumps, rotating shafts, fluid flow through pipes, etc.—which may be high frequency or low frequency), gas composition (e.g., of a wellhead area, personnel zone, near storage tanks, etc.—where low frequency may typically be acceptable, and/or it may be acceptable that no data is taken during certain times such as when personnel are not present), and/or pressure values (which may vary significantly both in required resolution and frequency or sampling rate depending upon operations currently occurring in the system). Additionally, oil and gas production systems have high variability in network infrastructure, both according to the system (e.g., an offshore platform versus a long-term ground-based production facility) and according to the operations being performed by the system (e.g., a wellhead in production may have limited network access, while a drilling or fracturing operation may have significant network infrastructure at a site during operations). Accordingly, it can be seen that operations of a network management circuit, a related expert system, and/or a related machine learning algorithm, to update the sensor data transmission protocol can significantly enhance sensing operations in various oil or gas production systems.
1. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values from the plurality of sensors;
a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values to a storage target computing device according to a sensor data transmission protocol;
a transmission environment circuit structured to determine transmission conditions corresponding to the communication of the at least a portion of the plurality of sensor data values to the storage target computing device;
a network management circuit structured to update the sensor data transmission protocol in response to the transmission conditions; and
wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.
2. The system of clause 1, wherein the transmission conditions comprise environmental conditions relating to sensor communication of the plurality of sensor data values, and wherein the network management circuit is further structured to analyze the environmental conditions, and wherein updating the sensor data transmission protocol comprises modifying the manner in which the plurality of sensor data values are transmitted from the plurality of sensors to the storage target computing device.
3. The system of clause 1, further comprising:
a data collector communicatively coupled to at least a portion of the plurality of sensors and responsive to the sensor data transmission protocol;
wherein the system collaboration circuit is structured to receive the plurality of sensor data values from the at least a portion of the plurality of sensors; and
wherein the transmission conditions correspond to at least one network parameter corresponding to the communication of the plurality of sensor data values from the at least a portion of the plurality of sensors.
4. The system of clause 3, wherein the network management circuit is further structured to update the sensor data transmission protocol to modify the data collector to adjust a data collection rate for at least one of the plurality of sensors.
5. The system of clause 3, wherein the network management circuit is further structured to update the sensor data transmission protocol to modify a multiplexing schedule of the data collector.
6. The system of clause 3, wherein the network management circuit is further structured to update the sensor data transmission protocol to command an intermediate storage operation for at least a portion of the plurality of sensor data values.
7. The system of clause 3, wherein the network management circuit is further structured to update the sensor data transmission protocol to command further data collection for at least a portion of the plurality of sensors.
8. The system of clause 3, wherein the network management circuit is further structured to update the sensor data transmission protocol to modify the data collector to implement a multiplexing schedule.
9. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to adjust a network transmission parameter for at least a portion of the plurality of sensor values.
10. The system of clause 9, wherein the adjusted network transmission parameter comprises at least one parameter selected from the parameters consisting of:
a timing parameter;
a protocol selection;
a file type selection;
a streaming parameter selection; and
a compression parameter.
11. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to change a frequency of data transmitted.
12. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to change a quantity of data transmitted.
13. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to change a destination of data transmitted.
14. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to change a network protocol used to transmit the data.
15. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to add a redundant network path to transmit the data.
16. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to bond an additional network path to transmit the data.
17. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to re-arrange a hierarchical network to transmit the data.
18. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to rebalance a hierarchical network to transmit the data.
19. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to reconfigure a mesh network to transmit the data.
20. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to delay a data transmission time.
21. The system of clause 20, wherein the network management circuit is further structured to update the sensor data transmission protocol to delay the data transmission time to a lower cost transmission time.
22. The system of clause 1, wherein the network management circuit is further structured to update the sensor data transmission protocol to reduce the amount of information sent at one time over the network.
23. The system of clause 3, wherein the network management circuit is further structured to update the sensor data transmission protocol to adjust a frequency of data sent from a second data collector.
24. The system of clause 1, wherein the network management circuit is further structured to adjust an external data access frequency, and wherein the system collaboration circuit is responsive to the adjusted external data access frequency.
25. The system of clause 1, wherein the network management circuit is further structured to adjust an external data access timing value, and wherein the system collaboration circuit is responsive to the adjusted external data access timing value.
26. The system of clause 1, wherein the network management circuit is further structured to adjust a network utilization value.
27. The system of clause 26, wherein the network management circuit is further structured to adjust the network utilization value to utilize bandwidth at a lower cost bandwidth time.
28. The system of clause 1, wherein the network management circuit is further structured to enable utilizing a high-speed network.
29. The system of clause 1, wherein the network management circuit is further structured to request a higher cost bandwidth access, and to update the sensor transmission protocol in response to the higher cost bandwidth access.
30. The system of clause 1, wherein the network management circuit further comprises an expert system, and wherein the updating the sensor data transmission protocol is further in response to operations of the expert system.
31. The system of clause 1, wherein the network management circuit further comprises a machine learning algorithm, and wherein the updating the sensor data transmission protocol is further in response to operations of the machine learning algorithm.
32. The system of clause 31, wherein the machine learning algorithm is further structured to utilize feedback data comprising the transmission conditions.
33. The system of clause 32, wherein the feedback data further comprises at least a portion of the plurality of sensor values.
34. The system of clause 33, wherein the feedback data further comprises benchmarking data.
35. The system of clause 34, wherein the benchmarking data further comprises data selected from the list consisting of: a network efficiency, a data efficiency, a comparison with offset data collectors, a throughput efficiency, a data efficacy, a data quality, a data precision, a data accuracy, and a data frequency.
36. The system of clause 34, wherein the benchmarking data further comprises data selected from the list consisting of: an environmental response, a mesh networking coherence, a data coverage, a target coverage, a signal diversity, a critical response, and a motion efficiency.
37. The system of clause 1, wherein the transmission conditions corresponding to the communication comprise at least one condition selected from the conditions consisting of:
a mesh network needs to rearrange to balance throughput;
a parent node in a hierarchically arranged network has had a change in connectivity;
a network super-node in a hybrid peer-to-peer application-layer network has been replaced; and
a node in a mesh or hierarchical network has been detected as malicious.
38. The system of clause 1, wherein the transmission conditions corresponding to the communication comprise at least one condition selected from the conditions consisting of:
a mesh network peer forwarding packets has lost connectivity;
a mesh network peer forwarding packets has gained additional bandwidth;
a mesh network peer forwarding packets has had a reduction in bandwidth; and
a mesh network peer forwarding packets has regained connectivity.
39. The system of clause 1, wherein the transmission conditions corresponding to the communication comprise at least one condition selected from the conditions consisting of:
a cost of transmitting information has changed dynamically;
a change has been made in a hierarchical network arrangement to balance bandwidth use in a network tree;
a portion of the network relaying sampling data has had a change in permissions, authorization level, or credentials;
a current cost of delivering information over a network hop has changed;
a higher-bandwidth network connection type has become available;
a lower-cost network connection type has become available; and
a change has been made in a network topology.
40. The system of clause 1, wherein the transmission conditions corresponding to the communication comprise at least one condition selected from the conditions consisting of:
a data collection client has changed a data frequency requirement for at least one of the plurality of sensor values;
a data collection client has changed a data type requirement for at least one of the plurality of sensor values;
a data collection client has changed a sensor target for data collection; and
a data collection client has changed the storage target computing device.
41. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values from the plurality of sensors;
a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values over a network having a plurality of nodes to a storage target computing device according to a sensor data transmission protocol;
a transmission environment circuit structured to determine transmission feedback corresponding to the communication of the at least a portion of the plurality of sensor data values over the network; and
a network management circuit structured to update the sensor data transmission protocol in response to the transmission feedback;
wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.
42. The system of clause 41, wherein the system collaboration circuit is further structured to send an alert to at least one of the plurality of nodes in response to the updated sensor data transmission protocol.
43. The system of clause 41, wherein updating the sensor data transmission comprises at least one operation selected from the operations consisting of:
providing instructions to rearrange a mesh network comprising the plurality of nodes;
providing instructions to rearrange a hierarchical data network comprising the plurality of nodes;
rearranging a peer-to-peer data network comprising the plurality of nodes; and
rearranging a hybrid peer-to-peer data network comprising the plurality of nodes.
44. The system of clause 41, wherein updating the sensor data transmission comprises at least one operation selected from the operations consisting of:
providing instructions to reduce a quantity of data sent over the network;
providing instructions to adjust a frequency of data capture sent over the network;
providing instructions to time-shift delivery of at least a portion of the plurality of sensor values sent over the network; and
providing instructions to change a network protocol corresponding to the network.
45. The system of clause 41, wherein updating the sensor data transmission comprises at least one operation selected from the operations consisting of:
providing instructions to reduce a throughput of at least one device coupled to the network;
providing instructions to reduce a bandwidth use of the network;
providing instructions to compress data corresponding to at least a portion of the plurality of sensor values sent over the network;
providing instructions to condense data corresponding to at least a portion of the plurality of sensor values sent over the network;
providing instructions to summarize data corresponding to at least a portion of the plurality of sensor values sent over the network; and
providing instructions to encrypt data corresponding to at least a portion of the plurality of sensor values sent over the network.
46. The system of clause 41, wherein updating the sensor data transmission comprises at least one operation selected from the operations consisting of:
providing instructions to deliver data corresponding to at least a portion of the plurality of sensor values to a distributed ledger;
providing instructions to deliver data corresponding to at least a portion of the plurality of sensor values to a central server;
providing instructions to deliver data corresponding to at least a portion of the plurality of sensor values to a super-node; and
providing instructions to deliver data corresponding to at least a portion of the plurality of sensor values redundantly across a plurality of network connections.
47. The system of clause 41, wherein updating the sensor data transmission comprises providing instructions to deliver data corresponding to at least a portion of the plurality of sensor values to one of the plurality of components.
48. The system of clause 47, wherein the one of the plurality of components is communicatively coupled to the sensor providing the data corresponding to at least a portion of the plurality of sensor values.
49. The system of clause 41, wherein the system collaboration circuit is further structured to interpret a quality of service commitment, and wherein the network management circuit is further structured to update the sensor data transmission protocol further in response to the quality of service commitment.
50. The system of clause 41, wherein the system collaboration circuit is further structured to interpret a service level agreement, and wherein the network management circuit is further structured to update the sensor data transmission protocol further in response to the service level agreement.
51. The system of clause 41, wherein the network management circuit is further structured to update the sensor data transmission protocol to provide instructions to increase a quality of service value.
52. The system of clause 41, wherein the network comprises a mesh network, and wherein the network management circuit is further structured to update the sensor data transmission protocol to provide instructions to eject one of the plurality of nodes from the mesh network.
53. The system of clause 41, wherein the network comprises a peer-to-peer network, and wherein the network management circuit is further structured to update the sensor data transmission protocol to provide instructions to eject one of the plurality of nodes from the peer-to-peer network.
54. The system of clause 41, wherein the network management circuit is further structured to update the sensor data transmission protocol to cache at least a portion of the plurality of sensor values.
55. The system of clause 54, wherein the network management circuit is further structured to update the sensor data transmission protocol to communicate the cached at least a portion of the plurality of sensor values in response to at least one of:
a determination that the cached data is requested;
a determination that the network feedback indicates communication of the cached data is available; and
a determination that higher priority data is present that requires utilization of cache resources holding the cached data.
56. The system of clause 41, further comprising a data collector configured to receive the at least a portion of the plurality of sensor data values, wherein the at least a portion of the plurality of sensor data values comprises data provided by a plurality of the sensors, and wherein the transmission feedback comprises network performance information corresponding to the data collector.
57. The system of clause 41, further comprising:
a data collector configured to receive the at least a portion of the plurality of sensor data values, wherein the at least a portion of the plurality of sensor data values comprises data provided by a plurality of the sensors;
a second data collector communicatively coupled to the network; and
wherein the transmission feedback comprises network performance information corresponding to the second data collector.
58. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values from the plurality of sensors at a predetermined frequency;
a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values over a network having a plurality of nodes to a storage target computing device according to a sensor data transmission protocol, the sensor data transmission protocol including a predetermined hierarchy of data collection and the predetermined frequency;
a transmission environment circuit structured to determine transmission feedback corresponding to the communication of the at least a portion of the plurality of sensor data values over the network; and
a network management circuit structured to update the sensor data transmission protocol in response to the transmission feedback and further in response to benchmarking data;
wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.
59. The system of clause 58, wherein updating the sensor data transmission comprises at least one operation selected from the operations consisting of:
providing an instruction to change the sensors of the plurality of sensors;
providing an instruction to adjust the predetermined frequency;
providing an instruction to adjust a quantity of the plurality of sensor data values that are stored;
providing an instruction to adjust a data transmission rate of the communication of the at least a portion of the plurality of sensor data values;
providing an instruction to adjust a data transmission time of the communication of the at least a portion of the plurality of sensor data values; and
providing an instruction to adjust a networking method of the communication over the network.
60. The system of clause 58, wherein the benchmarking data further comprises data selected from the list consisting of: a network efficiency, a data efficiency, a comparison with offset data collectors, a throughput efficiency, a data efficacy, a data quality, a data precision, a data accuracy, and a data frequency.
61. The system of clause 58, wherein the benchmarking data further comprises data selected from the list consisting of: an environmental response, a mesh networking coherence, a data coverage, a target coverage, a signal diversity, a critical response, and a motion efficiency.
62. The system of clause 58, wherein the benchmarking data further comprises data selected from the list consisting of: a quality of service commitment, a quality of service guarantee, a service level agreement, and a predetermined quality of service value.
63. The system of clause 58, wherein the benchmarking data further comprises data selected from the list consisting of: a network interference value, a network obstruction value, and an area of impeded network connectivity.
64. The system of clause 58, wherein the transmission feedback comprises a communication interference value selected from the values consisting of:
an interference caused by a component of the system;
an interference caused by one of the sensors;
an interference caused by a metallic object;
an interference caused by a physical obstruction;
an attenuated signal caused by a low power condition; and
an attenuated signal caused by a network traffic demand in a portion of the network.
65. A system for self-organized, network-sensitive data collection in an industrial environment, the system comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
a sensor communication circuit structured to interpret a plurality of sensor data values from the plurality of sensors at a predetermined frequency;
a system collaboration circuit structured to communicate at least a portion of the plurality of sensor data values over a network having a plurality of nodes to a storage target computing device according to a sensor data transmission protocol;
a transmission environment circuit structured to determine transmission feedback corresponding to the communication of the at least a portion of the plurality of sensor data values over the network;
a network management circuit structured to update the sensor data transmission protocol in response to the transmission feedback; and
a network notification circuit structured to provide an alert value in response to the updated sensor data transmission protocol;
wherein the system collaboration circuit is further responsive to the updated sensor data transmission protocol.
66. The system of clause 65, where the transmission feedback comprises at least one feedback value selected from the values consisting of: a change in transmission pricing, a change in storage pricing, a loss of connectivity, a reduction of bandwidth, a change in connectivity, a change in network availability, a change in network range, a change in wide area network (WAN) connectivity, and a change in wireless local area network (WLAN) connectivity.
67. The system of clause 66, wherein the network management circuit further comprises an expert system, and wherein the updating the sensor data transmission protocol is further in response to operations of the expert system.
68. The system of clause 66, wherein the expert system comprises at least one system selected from the systems consisting of: a rule-based system, a model-based system, a neural-net system, a Bayesian-based system, a fuzzy logic-based system, and a machine learning system.
69. The system of clause 65, wherein the network management circuit further comprises a machine learning algorithm, and wherein the updating the sensor data transmission protocol is further in response to operations of the machine learning algorithm.
70. The system of clause 69, wherein the machine learning algorithm is further structured to utilize feedback data comprising the transmission conditions.
71. The system of clause 70, wherein the feedback data further comprises at least a portion of the plurality of sensor values.
72. The system of clause 71, wherein the feedback data further comprises benchmarking data.
73. The system of clause 72, wherein the benchmarking data further comprises data selected from the list consisting of: a network efficiency, a data efficiency, a comparison with offset data collectors, a throughput efficiency, a data efficacy, a data quality, a data precision, a data accuracy, and a data frequency.
74. The system of clause 73, wherein the benchmarking data further comprises data selected from the list consisting of: an environmental response, a mesh networking coherence, a data coverage, a target coverage, a signal diversity, a critical response, and a motion efficiency.
Referencing
The example system 12500 further includes a sensor communication circuit 12522 (reference
In certain embodiments, sensor data values 12542 are provided to a data collector 12508 (
Referencing
For example, data from a temperature sensor may be planned to be stored locally on a sensor having storage capacity, and transmitted in bursts to a data controller. The data controller may be instructed to transmit the sensor data to the cloud computing device on a schedule, for example as the data controller memory reaches a threshold, as network communication capacity is available, at the conclusion of a process, and/or upon request. Additionally or alternatively, data from the sensors may be changed on a device or upon transfer of the data (e.g., just before transfer, just after transfer, or on a schedule). For example, the data storage profile 12532 may describe storing high resolution, high precision, and/or high sampling rate data, and reducing the storage of the data set after a period of time, a selected event, and/or confirmation of a successful process or that the high resolution data is no longer needed. Accordingly, higher resolution data and/or data from a large number of sensors may be available for utilization, such as by a sensor fusion operation or the like, while the long-term memory utilization is also managed. Each of the sensor data sets may be treated individually for memory storage characteristics, and/or sensors may be grouped for similar treatment (e.g., sensors having similar data characteristics and/or impact on the system, sensors cooperating in a sensor fusion operation, a group of sensors utilized for a model or a virtual sensor, etc.). In certain embodiments, sensor data from a single sensor may be treated distinctly according to an update of the data storage profile 12532, a time or process stage at which the data is taken, and/or a system condition such as a network issue, a fault condition, or the like. Additionally or alternatively, a single set of sensor data may be stored in multiple places in the system, for example where the same data is utilized in several separate sensor fusion operations, and the resource consumption from storing multiple sets of the same data is lower than a processor or network utilization to utilize a single stored data set in several separate processes.
Referencing
The example controller 12512 further includes a sensor data storage implementation circuit 12526 that stores at least a portion of the number of sensor data values in response to the data storage profile 12532. An example controller 12512 includes the data storage profile 12532 having a storage location definition 12534 corresponding to at least one of the number of sensor data values 12542, including at least one location such as: a sensor storage location (e.g., data stored for a period of time on the sensor, and/or on a portable device for a user 12518 in proximity to the industrial system 12502 where the portable device is adapted by the system as a sensor), a sensor communication device storage location (e.g., a data collector 12508, MUX device, smart sensor in communication with other sensors, and/or on a portable device for a user 12518 in proximity to the industrial system 12502 or a network of the industrial system 12502 where the portable device is adapted by the system as a communication device to transfer sensor data between components in the system, etc.), a regional network storage location (e.g., on a plant computer 12510 and/or controller 12512), and/or a global network storage location (e.g., on a cloud computing device 12514).
An example controller 12512 includes the data storage profile 12532 including a storage time definition 12536 corresponding to at least one of the number of sensor data values 12542, including at least one time value such as: a time domain description over which the corresponding at least one of the number of sensor data values is to be stored (e.g., times and locations for the data, which may include relative time to some aspect such as the time of data sampling, a process stage start or stop time, etc., or an absolute time such as midnight, Saturday, the first of the month, etc.); a time domain storage trajectory including a number of time values corresponding to a number of storage locations over which the corresponding at least one of the number of sensor data values is to be stored (e.g., the flow of the sensor data through the system across a number of devices, with the time for each storage transfer including a relative or absolute time description); a process description value over which the corresponding at least one of the number of sensor data values is to be stored (e.g., including a process description and the planned storage location for data values during the described process portion; the process description can include stages of a process, and identification of which process is related to the storage plan, and the like); and/or a process description trajectory including a number of process stages corresponding to a number of storage locations over which the corresponding at least one of the number of sensor data values is to be stored (e.g., the flow of the sensor data through the system across a number of devices, with process stage and/or process identification for each storage transfer).
An example controller 12512 includes the data storage profile 12532 including a data resolution description 12540 corresponding to at least one of the number of sensor data values 12542, where the data resolution description 12540 includes a value such as: a detection density value corresponding to the at least one of the number of sensor data values (e.g., detection density may be time sampling resolution, spatial sampling resolution, precision of the sampled data, and/or a processing operation to be applied that may affect the available resolution, such as filtering and/or lossy compression of the data); a detection density value corresponding to a more than one of the number of the sensor data values (e.g., a group of sensors having similar detection density values, a secondary data value determined from a group of sensors having a specified detection density value, etc.); a detection density trajectory including a number of detection density values of the at least one of the number of sensor data values, each of the number of detection density values corresponding to a time value (e.g., any of the detection density concepts combined with any of the time domain concepts); a detection density trajectory including a number of detection density values of the at least one of the number of sensor data values, each of the number of detection density values corresponding to a process stage value (e.g., any of the detection density concepts combined with any of the process description or stage concepts); and/or a detection density trajectory comprising a number of detection density values of the at least one of the number of sensor data values, each of the number of detection density values corresponding to a storage location value (e.g., detection density can be varied according to the device storing the data).
An example sensor data storage profile circuit 12524 further updates the data storage profile 12532 after the operations of the sensor data storage implementation circuit 12526, where the sensor data storage implementation circuit 12526 further stores the portion of the number of sensor data values 12542 in response to the updated data storage profile 12532. For example, during operations of a system at a first point in time, the sensor data storage implementation circuit 12526 utilizes a currently existing data storage profile sensor data storage implementation circuit 12526, which may be based on initial estimates of the system performance, desired data from an operator of the system, and/or from a previous operation of the sensor data storage profile circuit 12524. During operations of the system, the sensor data storage implementation circuit 12526 stores data according to the data storage profile 12532, and the sensor data storage profile circuit 12524 determines parameters for the data storage profile 12532 which may result in improved performance of the system. An example sensor data storage profile circuit 12524 tests various parameters for the data storage profile 12532, for example utilizing a machine learning optimization routine, and upon determining that an improved data storage profile 12532 is available, the sensor data storage profile circuit 12524 provides the updated data storage profile 12532 which is utilized by the sensor data storage implementation circuit 12526. In certain embodiments, the sensor data storage profile circuit 12524 may perform various operations such as supplying an intermediate data storage profile 12532 which is utilized by the sensor data storage implementation circuit 12526 to produce real-world results, applies modeling to the system (either first principles modeling based on system characteristics, a model utilizing actual operating data for the system, a model utilizing actual operating data for an offset system, and/or combinations of these) to determine what an outcome of a given data storage profile 12532 will be or would have been (including, for example, taking extra sensor data beyond what is utilized to support a process operated by the system), and/or applying randomized changes to the data storage profile 12532 to ensure that an optimization routine does not settle into a local optimum or non-optimal condition.
An example sensor data storage profile circuit 12524 further updates the data storage profile 12532 in response to external data 12544 and/or cloud-based data 12538, including data such as: an enhanced data request value (e.g., an operator, model, optimization routine, and/or other process requests enhanced data resolution for one or more parameters); a process success value (e.g., indicating that current storage practice provides for sufficient data availability and/or system performance; and/or that current storage practice may be over-capable, and one or more changes to reduce system utilization may be available); a process failure value (e.g., indicating that current storage practices may not provide for sufficient data availability and/or system performance, which may include additional operations or alerts to an operator to determine whether the data transmission and/or availability contributed to the process failure); a component service value (e.g., an operation to adjust the data storage to ensure higher resolution data is available to improve a learning algorithm predicting future service events, and/or to determine which factors may have contributed to premature service); a component maintenance value (e.g., an operation to adjust the data storage to ensure higher resolution data is available to improve a learning algorithm predicting future maintenance events, and/or to determine which factors may have contributed to premature maintenance); a network description value (e.g., a change in the network, for example by identification of devices, determination of protocols, and/or as entered by a user or operator, where the network change results in a capability change and potentially a distinct optimal storage plan for sensor data); a process feedback value (e.g., one or more process conditions detected); a network feedback value (e.g., one or more network changes as determined by actual operations of the network—e.g., a loss or reduction in communication of one or more devices, a network communication volume change, a transmission noise value change on the network, etc.); a sensor feedback value (e.g., metadata such as a sensor fault, capability change; and/or based on the detected data from the system, for example an anomalous reading, rate of change, or off-nominal condition indicating that enhanced or reduced resolution, sampling time, etc. should change the storage plan); and/or a second data storage profile, where the second data storage profile was generated for an offset system.
An example storage planning circuit 12528 determines a data configuration plan 12546 and updates the data storage profile 12532 in response to the data configuration plan 12546, where the sensor data storage implementation circuit 12526 further stores at least a portion of the number of sensor data values in response to the updated data storage profile 12532. An example data configuration plan 12546 includes a value such as: a data storage structure value (e.g., a data type—such as integer, string, a comma delimited file, how many bits are committed to the values, etc.); a data compression value (e.g., whether to compress data, a compression model to use, and/or whether segments of data can be replaced with summary information, polynomial or other curve fit summarizations, etc.); a data write strategy value (e.g., whether to store values in a distributed manner or on a single device, which network communication and/or operating system protocols to utilize); a data hierarchy value (e.g., which data is favored over other data where storage constraints and/or communication constraints will limit the stored data—the limits may be temporal, such as data will not be in the intended location at the intended time, or permanent, such as some data will need to be compressed in a lossy manner, and/or lost); an enhanced access value determined for the data (e.g., the data is of a type for reports, searching, modeling access, and/or otherwise tagged, where enhanced access includes where the data is stored for scope of availability, indexing of data, summarization of data, topical reports of data, which may be stored in addition to the raw or processed sensor data); and/or an instruction value corresponding to the data (e.g., a placeholder indicating where data can be located, an interface to access the data, metadata indicating units, precision, time frames, processes in operation, faults present, outcomes, etc.).
It can be seen that the provision of control over data flow and storage through the system allows for improvement generally, and movement toward optimization over time, of data management throughout the system. Accordingly, more data of a higher resolution can be accumulated, and in a more readily accessible manner, than previously known systems with fixed or manually configurable data storage and flow for a given utilization of resources such as storage space, communication bandwidth, power consumption, and/or processor execution cycles. Additionally, the system can respond to process variations that affect the optimal or beneficial parameters for controlling data flow and storage. One of skill in the art, having the benefit of the disclosures herein, will recognize that combinations of control of data storage schemes with data type control and knowledge about process operations for a system create powerful combinations in certain contemplated embodiments. For example, data of a higher resolution can be maintained for a longer period and made available if a need for the data arises, without incurring the full cost of storing the data permanently and/or communicating the data throughout every layer of the system.
In an embodiment, in an underground mining inspection system, certain detailed data regarding toxic gas concentrations, temperatures, noise, etc. may need to be captured and stored for regulatory purposes, but for ongoing operational purposes, perhaps only a single data point regarding a one or more toxic gases is needed periodically. In this embodiment, the data storage profile for the system may indicate that only certain sensor data aligned with regulatory needs be stored in a certain manner that is long term and optionally only available as needed, while other sensor data required operationally be stored in a more accessible manner.
In another embodiment involving automotive brakes for fleet vehicles, data regarding brake use and performance may be acquired at high resolution and stored in a first data storage that is not transmitted throughout the network, while lower resolution data are transmitted periodically and/or in near real time to a fleet control and maintenance application. Should the application or other user require higher resolution data, it may be accessed from the first data storage.
In a further embodiment of manufacturing body and frame components of trucks and cars, certain detailed data regarding paint color, surface curvature, and other quality control measures may be captured and stored at high resolution, but for ongoing operational purposes, only low resolution data regarding throughput are transmitted. In this embodiment, the data storage profile for the system may indicate that only certain sensor data aligned with quality control needs be stored in a certain manner that is long term and optionally only available as needed, while other sensor data required operationally be stored in a more accessible manner.
In another example, data types, resolution, and the like can be configured and changed as the data flows through the system, according to values that are beneficial for the individual components handling the data, according to the utilized networking resources for the data, and/or according to accompanying data (e.g., a model, virtual sensor, and/or sensor fusion operation) where higher capability data would not improve the precision of the process utilizing the accompanying data.
In an embodiment, in a rail condition monitoring systems, as rail condition data are acquired, each component of the system may require different resolutions of the same data. Continuing with this example, as real-time rail traffic data are acquired, these data may be stored and/or transmitted at low resolution in order to quickly disseminate the data throughout the system, while utilization and load data may be stored and utilized at higher resolution to track rail use fees and need for rail maintenance at a more granular level.
In another embodiment of a hydraulic pump operating in a tractor, as the tractor is in the field and does not have access to a network, data from on-board sensors may be acquired and stored in a local manner on the tractor at low resolution, but when the tractor regains access, data may be acquired and transmitted at high resolution.
In yet another embodiment of an actuator in a robotic handling unit in an automotive plant, data regarding the actuator may flow into multiple downstream systems, such as a production tracking system that utilizes the actuator data alone and an energy efficiency tracking system that utilizes the data in a sensor fusion with data from environmental sensors. Resolution of the actuator data may be configured differently as it is transmitted to each of these systems for their disparate uses.
In still another embodiment of a generator in a mine, data may be acquired regarding the performance of the generator, carbon monoxide levels near the generator and a cost for running the generator. Each component of a control system overseeing the mine may require different resolutions of the same data. Continuing with this example, as carbon monoxide data are acquired, these data may be stored and/or transmitted at low resolution in order to quickly disseminate the data throughout the system in order to properly alert workers. Performance and cost data may be stored and utilized at higher resolution to track economic efficiency and lifetime maintenance needs.
In an additional embodiment, sensors on a truck's wheel end may monitor lubrication, noise (e.g. grinding, vibration) and temperature. While in the field, sensor data may be transmitted remotely at low resolution for remote monitoring, but when within a threshold distance from a fleet maintenance facility, data may be transmitted at high resolution.
In another example, accompanying information for the data allows for efficient downstream processing (e.g., by a downstream device or process accessing the data) including unpackaging the data, readily determining where related higher capability data may be present in the system, and/or streamlining operations utilizing the data (e.g., reporting, modeling, alerting, and/or performing a sensor fusion or other system analysis). An embodiment includes storing high capability (e.g., high sampling rate, high precision, indexed, etc.) in a first storage device in the system (e.g., close to the sensors in the network layer to preserve network communication resources) and sending lower capability data up the network layers (e.g., to a cloud-computing device), where the lower capability data includes accompanying information to access the stored high capability data, including accompanying data that may be accessible to a user (e.g., a header, message box, or other organically interfaceable accompanying data) and/or accessible to an automated process (e.g., structured data, XML, populated fields, or the like) where the process can utilize the accompanying data to automatically request, retrieve, or access the high capability data. In certain embodiments, accompanying data may further include information about the content, precision, sampling time, calibrations (e.g., de-bouncing, filtering, or other processing applied) such that an accessing component or user can determine without retrieving the high capability data whether such data will meet the desired parameters.
In an embodiment, vibration noise from vibration sensors attached to vibrators on an assembly line may be stored locally in a high resolution format while a low resolution version of the same data with accompanying information regarding the availability of ambient and local noise data for a sensor fusion may be transmitted to a cloud-based server. If a resident process on the server requires the high resolution data, such as a machine learning process, the server may retrieve the data at that time.
In another embodiment of an airplane engine, performance data aggregated from a plurality of sensors may be transmitted while in flight along with accompanying information to a remote site. The accompanying information, such as a header with metadata relating to historical plane information, may allow the remote site to efficiently analyze the performance data in the context of the historical data without having to access additional databases.
In a further embodiment of a coal crusher in a power generation facility, data accompanying low quality sensor data regarding the size of coal exiting the crusher may include information about the precision in the size measurement such that a technician can determine if the higher resolution data are needed to confirm a determination that the crusher needs to come offline for maintenance.
In yet a further embodiment of a drilling machine or production platform employed in oil and gas production, high capability data may be acquired and stored locally regarding parameters of the drill's and platform's operation, but only low capability data are transmitted off-site to conserve bandwidth. Along with the low capability data, accompanying information may include instructions on how an automated off-site process can automatically access the high capability data in the event that it is required.
In still a further embodiment, temperature sensors on a pump employed in oil & gas production or mining may be stored locally in a high resolution format while a low resolution version of the same data with accompanying information regarding the availability of noise and energy use data for a sensor fusion may be transmitted to a cloud-based server. If a resident process on the server requires the high resolution data, such as a machine learning process, the server may retrieve the data at that time.
In another embodiment of a gearbox in an automatic robotic handling unit or an agricultural setting, performance data aggregated from a plurality of sensors may be transmitted while in use along with accompanying information to a remote site. The accompanying information, such as a header with metadata relating to historical gearbox information, may allow the remote site to efficiently analyze the performance data in the context of the historical data without having to access additional databases.
In a further embodiment of a ventilation system in a mine, data accompanying low quality sensor data regarding the size of particulates in the air may include information about the precision in the size measurement such that a technician can determine if the higher resolution data are needed to confirm a determination that the ventilation system requires maintenance.
In yet a further embodiment of a rolling bearing employed in agriculture, high capability data may be acquired and stored locally regarding parameters of the rolling bearing's operation, but only low capability data are transmitted off-site to conserve bandwidth. Along with the low capability data, accompanying information may include instructions on how an automated off-site process can automatically access the high capability data in the event that it is required.
In a further embodiment of a stamp mill in a mine, data accompanying low quality sensor data regarding the size of mineral deposits exiting the stamp mill may include information about the precision in the size measurement such that a technician can determine if the higher resolution data are needed to confirm a determination that the stamp mill requires a change in an operation parameter.
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An example system 12500 further includes a haptic feedback circuit 12530 that determines a haptic feedback instruction 12548 in response to at least one of the number of sensor values 12542 and/or the data storage profile 12532, and a haptic feedback device 12516 responsive to the haptic feedback instruction 12548 (
In certain embodiments, the haptic feedback circuit 12530 provides a haptic feedback instruction 12548 as an alert or notification to the user 12518—for example to alert or notify the user 12518 that a process has commenced or is about to start, that an off-nominal operation is detected or predicted, that a component of the system requires or is predicted to require maintenance, that an aspect of the system is in a condition that the user 12518 may want to be aware of (e.g., a component is still powered, has high potential energy of any type, is at a high pressure, and/or is at a high temperature—where the user 12518 may be in proximity to the component), that a data storage related aspect of the system is in a noteworthy condition (e.g., a data storage component of the system is at capacity, out of communication, is in a fault condition, has lost contact with a sensor, etc.), to request a response from the user 12518 (e.g., an approval to start a process, data transfer, process rate change, clear a fault, etc.). In certain embodiments, the haptic feedback circuit 12530 configures the haptic feedback instruction 12548 to provide an intuitive feedback to the user 12518. For example: an alert value may provide a more rapid, urgent, and/or intermittent vibration mode relative to an informational notification; a temperature based alert or notification may utilize a temperature based haptic feedback (e.g., an overtemperature vessel notification may provide a warm or cold haptic feedback) and/or flashing a color that is associated with the temperature (e.g., flashing red for an overtemperature or blue for an under-temperature); an electrically based notification may provide an electrically associated haptic feedback (e.g., a sound associated with electricity such as a buzzing or sparking sound, or even a mild electrical feedback such as when a user is opening a panel for a component that is still powered); providing a vibration feedback for a bearing, motor, or other rotating or vibrating component that is operating off-nominally; and/or providing a requested feedback to the user based upon sensed data (e.g., transmitting a vibration profile to the haptic feedback device that is analogous to the detected vibration in a requested component—for example allowing an expert user to diagnose the component without physical contact; providing a haptic feedback for a requested component—for example if the user is double checking a lockout/tagout operation before entering a component, opening a panel, and/or entering a potentially hazardous area). The provided examples for operations of the haptic feedback circuit 12530 are non-limiting illustrations.
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An example system includes the network coding circuit 12568 further determining a network definition value 12572, and providing the network coding value 12570 further in response to the network definition value 12572. Example network definition values 12572 include values such as: a network feedback value (e.g., transfer rates, up time, synchronization availability, etc.); a network condition value (e.g., presence of noise, transmission/receiver capability, drop-outs, etc.); a network topology value (e.g., the communication flow and connectivity of devices; operating systems, protocols, and storage types of devices; available computing resources on devices; the location and function of devices in the system); an intermittently available network device value (e.g., a known or observed availability for the device over time or process stage; predicted availability of the device; prediction of known noise factors for the device, such as process operations that reduce device availability); and/or a network cost description value (e.g., resource utilization of the device, including relative cost or impact of processing, memory, and/or communication resources; power utilization and cost of power consumption for devices; available power for the device and a cost description for externalities related to consuming the power—such as for a battery where the power itself may not be expensive but the power in the specific location has a cost associated with replacement, including availability or access to the device during operations).
An example system includes the network coding circuit 12568 further providing the network coding value 12570 such that the sensor data storage implementation circuit stores a first portion of the number of sensor data values 12542 utilizing a first network coding value 12570, and a second portion of the number of sensor data values 12570 utilizing a second network coding value 12570 (e.g., the network coding values 12570 can vary with the data being transmitted, the transmitting device, and/or over time or process stage). Example and non-limiting network coding values include: a network type selection (e.g., public, private, wireless, wired, intranet, external, internet, cellular, etc.), a network selection (e.g., which one or more of an available number of networks will be utilized), a network coding selection (e.g., packet definitions, encoding techniques, linear, randomized linear, convolution, triangulated, etc.), a network timing selection (e.g., synchronization and sequencing of data transmissions between devices), a network feature selection (e.g., turning on or off network support devices or repeaters; enabling, disabling, or adjusting security selections; increasing or decreasing a power of a device, etc.), a network protocol selection (e.g., TCP/IP, FTP, Wi-Fi, Bluetooth, Ethernet, and/or routing protocols); a packet size selection (including header and/or parity information); and/or a packet ordering selection (e.g., determining how to transmit the various sensor information that may be on a device, and/or determining the packet to data value correspondence). An example network coding circuit 12568 further adjusts the network coding value 12570 to provide an intermediate network coding value (e.g., as a test coding value on the system, and/or as a modeled coding value being run off-line), to compare a performance indicator 12574 corresponding to each of the network coding value 12570 and the intermediate network coding value, and to provide an updated network coding value (e.g., as the network coding value 12570) in response to the comparison of the performance indicators 12574.
An example system includes an industrial system having a number of components, and a number of sensors each operatively coupled to at least one of the number of components. The number of sensors provide a number of sensor values, and the system further includes a number of organizing structures such as a controller, a data collector, a plant computer, a cloud-based server and/or global computing device, and/or a network layer, where the organizing structures are configured for self-organizing storage of at least a portion of the number of sensor values. For example, operations of the controller 12512 provide for storage and distribution of sensor data values to reduce consumption of resources (processor, network, and/or memory) for storing sensor data. The self-organizing operations include management of the stored sensor data over time, including providing sensor information to system components in time to complete operations therefore (e.g., control, improvement, modeling, and/or machine learning for process operations of the system). Additionally, data security, including long-term security due to storage media, geographic, and/or unauthorized access, is considered throughout the data storage life cycle. An example system further includes the organizing structures providing enhanced resolution of the number of sensor values in response to at least one of an enhanced data request value or an alert value corresponding to the industrial system. The system provides enhanced resolution by controlling the storage processes to address system impact, including keeping lower resolution, summary, or other accessibility data available, and storing higher resolution data in a lower resource utilization manner which is available upon request and/or at a time appropriate to system operations. Example enhanced resolution includes: an enhanced spatial resolution, an enhanced time domain resolution, a greater number of the number of sensor values than a standard resolution of the number of sensor values, and/or a greater precision of at least one of the number of sensor values than a standard resolution of the number of sensor values. An example system further includes a network layer, where the organizing structures are configured for self-organizing network coding for communication of the number of sensor values on the network layer. An example system further includes a haptic feedback device of a user in proximity to at least one of the industrial system or the network layer, and where the organizing structures are configured for providing haptic feedback to the haptic feedback device, and/or for configuring the haptic feedback to provide an intuitive alert to the user.
1. A system for data collection in an industrial environment, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
2. The system of clause 1, wherein the data storage profile comprises a storage location definition corresponding to at least one of the plurality of sensor data values, the storage location definition comprising at least one location selected from the locations consisting of: a sensor storage location, a sensor communication device storage location, a regional network storage location, and a global network storage location.
3. The system of clause 1, wherein the data storage profile comprises a storage time definition corresponding to at least one of the plurality of sensor data values, the storage time definition comprising at least one time value selected from the time values consisting of:
a time domain description over which the corresponding at least one of the plurality of sensor data values is to be stored;
a time domain storage trajectory comprising a plurality of time values corresponding to a plurality of storage locations over which the corresponding at least one of the plurality of sensor data values is to be stored;
a process description value over which the corresponding at least one of the plurality of sensor data values is to be stored; and
a process description trajectory comprising a plurality of process stages corresponding to a plurality of storage locations over which the corresponding at least one of the plurality of sensor data values is to be stored.
4. The system of clause 1, wherein the data storage profile comprises a data resolution description corresponding to at least one of the plurality of sensor data values, wherein the data resolution description comprises at least one of:
a detection density value corresponding to the at least one of the plurality of sensor data values;
a detection density value corresponding to a plurality of the at least one of the plurality of the sensor data values;
a detection density trajectory comprising a plurality of detection density values of the at least one of the plurality of sensor data values, each of the plurality of detection density values corresponding to a time value;
a detection density trajectory comprising a plurality of detection density values of the at least one of the plurality of sensor data values, each of the plurality of detection density values corresponding to a process stage value;
and
a detection density trajectory comprising a plurality of detection density values of the at least one of the plurality of sensor data values, each of the plurality of detection density values corresponding to a storage location value.
5. The system of clause 1, wherein the sensor data storage profile circuit is further structured to update the data storage profile after the operations of the sensor data storage implementation circuit, and wherein the sensor data storage implementation circuit is further structured to store the portion of the plurality of sensor data values in response to the updated data storage profile.
6. The system of clause 1, wherein the sensor data storage profile circuit is further structured to update the data storage profile in response to external data, the external data comprising at least one data value selected from the data values consisting of:
an enhanced data request value;
a process success value;
a process failure value;
a component service value;
a component maintenance value;
a network description value;
a process feedback value;
a network feedback value;
a sensor feedback value; and
a second data storage profile, the second data storage profile generated for an offset system.
7. The system of clause 1, further comprising a storage planning circuit structured to determine a data configuration plan, to update the data storage profile in response to the data configuration plan, and wherein the sensor data storage implementation circuit is further structured to store the at least a portion of the plurality of sensor data values in response to the updated data storage profile.
8. The system of clause 7, wherein the data configuration plan further comprises at least one value selected from the values consisting of:
a data storage structure value;
a data compression value;
a data write strategy value;
a data hierarchy value;
an enhanced access value determined for the data; and
an instruction value corresponding to the data.
9. The system of clause 1, further comprising:
a haptic feedback circuit structured to determine a haptic feedback instruction in response to at least one of the plurality of sensor values or the data storage profile; and
a haptic feedback device responsive to the haptic feedback instruction.
10. The system of clause 9, wherein the haptic feedback instruction comprises at least one instruction selected from the instructions consisting of:
a vibration command;
a temperature command;
a sound command;
an electrical command; and
a light command
11. The system of clause 1, wherein the data storage plan is generated by a rule-based expert system utilizing feedback, wherein the feedback relates to one or more of an aspect of the industrial environment or the plurality of sensor data values.
12. The system of clause 1, wherein the data storage plan is generated by a model-based expert system utilizing feedback, wherein the feedback relates to one or more of an aspect of the industrial environment or the plurality of sensor data values.
13. The system of clause 1, wherein the data storage plan is generated by an iterative expert system utilizing feedback, wherein the feedback relates to one or more of an aspect of the industrial environment or the plurality of sensor data values.
14. The system of clause 1, wherein the data storage plan is generated by a deep learning machine system utilizing feedback, wherein the feedback relates to one or more of an aspect of the industrial environment or the plurality of sensor data values.
15. The system of clause 1, wherein the data storage plan is based on one or more an underlying physical media type of the storage, a type of device or system on which storage resides, and a mechanism by which storage can be accessed for reading or writing data.
16. The system of clause 15, wherein the underlying physical media is one of a tape media, a hard disk drive media, a flash memory media, a non-volatile memory, an optical media, and a one-time programmable memory.
17. The system of clause 15, wherein the data storage plan accounts for or specifies a parameter relating to the underlying physical media comprising one or more of a storage duration, a power usage, a reliability, a redundancy, a thermal performance factor, a robustness to environmental conditions, an input/output speed and capability, a writing speed, a reading speed, a data file organization, an operating system, a read-write life cycle, a data error rate, and a data compression aspect related to or inherent to the underlying physical media or a media controller.
18. The system of clause 1, wherein the data storage plan comprises one or more of a storage type plan, a storage media plan, a storage access plan, a storage protocol plan, a storage writing protocol plan, a storage security plan, a storage location plan, and a storage backup plan.
19. A system for data collection in an industrial environment, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values;
a network coding circuit structured to provide a network coding value in response to the plurality of sensor data values and the data storage profile; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile and the network coding value.
20. The system of clause 19, wherein the network coding circuit is further structured to determine a network definition value, and to provide the network coding value further in response to the network definition value, wherein the network definition value comprises at least one value selected from the values consisting of:
a network feedback value;
a network condition value;
a network topology value;
an intermittently available network device value; and
a network cost description value.
21. The system of clause 19, wherein the network coding circuit is further structured to provide the network coding value such that the sensor data storage implementation circuit stores a first portion of the plurality of sensor data values utilizing a first network coding value, and a second portion of the plurality of sensor data values utilizing a second network coding value.
22. The system of clause 19, wherein the network coding value comprises at least one of the values selected from the values consisting of: a network type selection, a network selection, a network coding selection, a network timing selection, a network feature selection, a network protocol selection, a packet size selection, and a packet ordering selection.
23. The system of clause 22, wherein the network coding circuit is further structured to adjust the network coding value to provide an intermediate network coding value, to compare a performance indicator corresponding to each of the network coding value and the intermediate network coding value, and to provide an updated network coding value in response to the comparison of the performance indicators.
24. A system, comprising:
an industrial system comprising a plurality of components, and a plurality of sensors each operatively coupled to at least one of the plurality of components;
the plurality of sensors providing a plurality of sensor values; and
a means for self-organizing storage of at least a portion of the plurality of sensor values.
25. The system of clause 24, further comprising:
a means for providing enhanced resolution of the plurality of sensor values in response to at least one of an enhanced data request value or an alert value corresponding to the industrial system; and
wherein the enhanced resolution comprises at least one of an enhanced spatial resolution, an enhanced time domain resolution, a greater number of the plurality of sensor values than a standard resolution of the plurality of sensor values, and a greater precision of at least one of the plurality of sensor values than the standard resolution of the plurality of sensor values.
26. The system of clause 25, further comprising a network layer, and a means for self-organizing network coding for communication of the plurality of sensor values on the network layer.
27. The system of clause 26, further comprising a means for providing haptic feedback to a haptic feedback device of a user in proximity to at least one of the industrial system or the network layer.
28. The system of clause 27, further comprising a means for configuring the haptic feedback to provide an intuitive alert to the user.
29. A system for self-organizing data storage for data collected from a mine, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
30. A system for self-organizing data storage for data collected from an assembly line, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
31. A system for self-organizing data storage for data collected from an agricultural system, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
32. A system for self-organizing data storage for data collected from an automotive robotic handling unit, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
33. A system for self-organizing data storage for data collected from an automotive system, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
34. A system for self-organizing data storage for data collected from an automotive robotic handling unit, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
35. A system for self-organizing data storage for data collected from an aerospace system, the system comprising
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
36. A system for self-organizing data storage for data collected from a railway, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
37. A system for self-organizing data storage for data collected from an oil and gas production system, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
38. A system for self-organizing data storage for data collected from a power generation system, the system comprising:
a sensor communication circuit structured to interpret a plurality of sensor data values;
a sensor data storage profile circuit structured to determine a data storage profile, the data storage profile comprising a data storage plan for the plurality of sensor data values; and
a sensor data storage implementation circuit structured to store at least a portion of the plurality of sensor data values in response to the data storage profile.
In embodiments, methods and systems are provided for data collection in or relating to one or more machines deployed in an industrial environment using self-organized network coding for network transmission of sensor data in a network. In embodiments, network coding may be used to specify and manage the manner in which packets (including streams of packets as noted in various embodiments disclosed throughout this disclosure and the documents incorporated by reference) are relayed from a sender (e.g., a data collector, instrumentation system, computer, or the like in an industrial environment where data is collected, such as from sensors or instruments on, in or proximal to industrial machines or from data storage in the environment) to a receiver (e.g., another data collector (such as in a swarm or coordinated group), instrumentation system, computer, storage, or the like in the industrial environment, or to a remote computer, server, cloud platform, database, data pool, data marketplace, mobile device (e.g., mobile phone, personal computer, tablet, or the like), or other network-connected device of system), such as via one or more network infrastructure elements (referred to in some cases herein as nodes), such as access points, switches, routers, servers, gateways, bridges, connectors, physical interfaces and the like, using one or more network protocols, such as IP-based protocols, TCP/IP, UDP, HTTP, Bluetooth, Bluetooth Low Energy, cellular protocols, L 1E, 2G, 3G, 4G, 5G, CDMA, TDSM, packet-based protocols, streaming protocols, file transfer protocols, broadcast protocols, multi-cast protocols, unicast protocols, and others. For situations involving bi-directional communication, any of the above-referenced devices or systems, or others mentioned throughout this disclosure, may play the role of sender or receiver, or both. Network coding may account for availability of networks, including the availability of multiple alternative networks, such that a transmission may be delivered across different networks, either separated into different components or sending the same components redundantly. Network coding may account for bandwidth and spectrum availability; for example, a given spectrum may be divided (such as with sub-dividing spectrum by frequency, by time-division multiplexing, and other techniques). Networks or components thereof may be virtualized, such as for purposes of provisioning of network resources, specification of network coding for a virtualized network, or the like. Network coding may include a wide variety of approaches as described in Appendix A, and in connection with Figures in Appendix A.
In embodiments, one or more network coding systems or methods of the present disclosure may use self-organization, such as to configure network coding parameters for one or more transmissions over one or more networks using an expert system, which may comprise a model-based system (such as automatically selecting network coding parameters or configuration based on one or more defined or measured parameters relating to a transmission, such as parameters of the data or content to be transmitted, the sender, the receiver, the available network infrastructure components, the conditions of the network infrastructure, the conditions of the industrial environment, or the like). A model may, for example, account for parameters relating to file size, numbers of packets, size of a stream, criticality of a data packet or stream, value of a packet or stream, cost of transmission, reliability of a transmission, quality of service, quality of transmission, quality of user experience, financial yield, availability of spectrum, input/output speed, storage availability, storage reliability, and many others as noted throughout this disclosure. In embodiments, the expert system may comprise a rule-based system, where one or more rules is executed based on detection of a condition or parameter, calculation of a variable, or the like, such as based on any of the above-noted parameters. In embodiments, the expert system may comprise a machine learning system, such as a deep learning system, such as based on a neural network, a self-organizing map, or other artificial intelligence approach (including any noted throughout this disclosure or the documents incorporated by reference). A machine learning system in any of the embodiments of this disclosure may configure one or more inputs, weights, connections, functions (including functions of individual neurons or groups of neurons in a neural net) or other parameters of an artificial intelligence system. Such configuration may occur with iteration and feedback, optionally involving human supervision, such as by feeding back various metrics of success or failure. In the case of network coding, configuration may involve setting one or more coding parameters for a network coding specification or plan, such as parameters for selection of a network, selection one or more nodes, selection of data path, configuration of timers or timing parameters, configuration of redundancy parameters, configuration of coding types (including use of regenerating codes, such as for use of network coding for distributed storage, such as in peer-to-peer networks, such as a peer-to-peer network of data collectors, or a storage network for a distributed ledger, as noted elsewhere in this disclosure), coefficients for coding (including linear algebraic coefficients), parameters for random or near-random linear network coding (including generation of near random coefficients for coding), session configuration parameters, or other parameters noted in the network coding embodiments described below, throughout this disclosure, and in the documents incorporated herein by reference. For example, a machine learning system may configure the selection of a protocol for a transmission, the selection of what network(s) will be used, the selection of one or more senders, the selection of one or more routes, the configuration of one or more network infrastructure nodes, the selection of a destination receiver, the configuration of a receiver, and the like. In embodiments, each one of these may be configured by an individual machine learning system, or the same system may configure an overall configuration by adjusting various parameters of one or more of the above under iteration, through a series of trials, optionally seeded by a training set, which may be based on human configuration of parameters, or by model-based and/or rule-based configuration. Feedback to a machine learning system may comprise various measures, including transmission success or failure, reliability, efficiency (including cost-based, energy-based and other measures of efficiency, such as measuring energy per bit transmitted, energy per bit stored, or the like), quality of transmission, quality of service, financial yield, operational effectiveness, success at prediction, success at classification, and others. In embodiments, a machine learning system may configure network coding parameters by predicting network behaviour or characteristics and may learn to improve prediction using any of the techniques noted above. In embodiments, a machine learning system may configure network coding parameters by classification of one or more network elements and/or one or more network behaviours and may learn to improve classification, such as by training and iteration over time. Such machine-based prediction and/or classification may be used for self-organization, including by model-based, rule-based, and machine learning-based configuration. Thus, self-organization of network coding may use or comprise various combinations or permutations of model-based systems, rule-based systems, and a variety of different machine-learning systems (including classification systems, prediction systems, and deep learning systems, among others).
As described in US patent application 2017/0013065, entitled “Cross-session network communication configuration,” network coding may involve methods and systems for data communication over a data channel on a data path between a first node and a second node and may include maintaining data characterizing one or more current or previous data communication connections traversing the data channel and initiating a new data communication connection between the first node and the second node including configuring the new data communication connection at least in part according to the maintained data. The maintained data may characterize one or more data channels on one or more data paths between the first node and the second node over which said one or more current or previous data communication connections pass. The maintained data may characterize an error rate of the one or more data channels. The maintained data may characterize a bandwidth of the one or more data channels. The maintained data may characterize a round trip time of the one or more data channels. The maintained data may characterize communication protocol parameters of the one or more current or previous data communication connections.
The communication protocol parameters may include one or more of a congestion window size, a block size, an interleaving factor, a port number, a pacing interval, a round trip time, and a timing variability. The communication protocol parameters may include two or more of a congestion window size, a block size, an interleaving factor, a port number, a pacing interval, a round trip time, and a timing variability.
The maintained data may characterize forward error correction parameters associated with the one or more current or previous data communication connections. The forward error correction parameters may include a code rate. Initiating the new data communication connection may include configuring the new data communication connection according to first data of the maintained data, the first data is maintained at the first node, and initiating the new data communication connection includes providing the first data from the first node to the second node for configuring the new data communication connection.
Initiating the new data communication connection may include configuring the new data communication connection according to first data of the maintained data, the first data is maintained at the first node, and initiating the new data communication connection includes accessing first data at the first node for configuring the new data communication connection. Any one of these elements of maintained data, including various parameters of communication protocol, error correction parameters, connection parameters, and others, may be provided to the expert system for supporting self-organization of network coding, including for execution of rules to set network coding parameters based on the maintained data, for population of a model, or for configuration of parameters of a neural net or other artificial intelligence system.
Initiating the new data communication connection may include configuring the new data communication connection according to first data of the maintained data, the first data being maintained at the first node, and initiating the new data communication connection includes accepting a request from the first node for establishing the new data communication connection between the first node and the second node, including receiving, at the second node, at least one message from the first node comprising the first data for configuring said connection. The method may include maintaining the new data communication connection between the first node and the second node, including maintaining communication parameters, including initializing said communication parameters according the first data received in the at least one message from the first node.
Maintaining the new data communication connection may include adapting the communication parameters according to feedback from the first node. The feedback from the first node may include feedback messages received from the first node. The feedback may include feedback derived from a plurality of feedback messages received from the first node. Feedback may relate to any of the types of feedback noted above, and may be used for self-organizing the data communication connection using the expert system.
In some examples, one or more training communication connections over a data channel on a data path are employed prior to establishment of data communication connections over the data channel on the data path. The training communication connections are used to collect information about the data channel which is then used when establishing the data communication connections. In other examples, no training communication connections are employed and information about the data channel is obtained from one or more previous or current data communication connection over the data channel on the data path.
1. A method for data communication over a data channel on a data path between a first node and a second node, the method comprising:
maintaining data characterizing one or more current or previous data communication connections traversing the data channel; and
initiating a new data communication connection between the first node and the second node including configuring the new data communication connection at least in part according to the maintained data, wherein the configuration of the new data communication connection is configured by an expert system.
2. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the configuration.
3. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to the data channel.
4. The method of clause 3 wherein the expert system takes a plurality of inputs from a data collector that accepts data about a machine operating in an industrial environment.
As described in US patent application 2017/0012861, entitled “Multi-path network communication,” self-organized network coding under control of an expert system may involve methods and systems for data communication between a first node and a second node over a number of data paths coupling the first node and the second node and may include transmitting messages between the first node and the second node over the number of data paths, including transmitting a first subset of the messages over a first data path of the number of data paths and transmitting a second subset of the messages over a second data path of the number of data paths. In situations where the first data path has a first latency and the second data path has a second latency substantially larger than the first latency, and messages of the first subset of the messages are chosen to have first message characteristics and messages of the second subset are chosen to have second message characteristics, different from the first message characteristics.
Messages having the first message characteristics, targeted for data paths of lower latency, may include time critical messages; for example, in an industrial environment, messages relating to a critical fault condition of a machine (e.g., overheating, excessive vibration, or any of the other fault conditions described throughout this disclosure) or relating to a safety hazard, or a time-critical operational step on which other processes depend (e.g., completion of a catalytic reaction, completion of a sub-assembly, or the like in a high-value, high-speed manufacturing process, a refining process, or the like) may be designated as time critical (such as by a rule that can be parsed or processed by a rules engine) or may be learned to be time-critical by the expert system, such as based on feedback regarding outcomes over time, including outcomes for similar machines having similar data in similar industrial environments. The first subset of the messages and the second subset of the messages may be determined from a portion of the messages available at the first node at a time of transmission. At a subsequent time of transmission, additional messages made available to the first node may be divided into the first subset and the second subset based on message characteristics associated with the additional messages. Division into subsets and selection of what subsets are targeted to what data path may be undertaken by an expert system. Messages having the first message characteristics may be associated with an initial subset of a data set and messages having the second message characteristics may be associated with a subsequent subset of the data set. The methods and systems described herein for selecting inputs for data collection and for multiplexing data may be organized, such as by an expert system, to configure inputs for the alternative channels, such as by providing streaming elements that have real-time significance to the first data path and providing other elements, such as for long-term, predictive maintenance, to the other data path. In embodiments, the messages of the second subset may include messages that are at most n messages ahead of a last acknowledged message in a sequential transmission order associated with the messages, wherein n is determined based on a buffer size at one of the first and second nodes.
Messages having the first message characteristics may include acknowledgement messages and messages having the second message characteristics may include data messages. Messages having the first message characteristics may include supplemental data messages. The supplemental data messages may include data messages may include redundancy data and messages having the second message characteristics may include original data messages. The first data path may include a terrestrial data path and the second data path may include a satellite data path. The terrestrial data path may include one or more of a cellular data path, a digital subscriber line (DSL) data path, a fiber optic data path, a cable internet based data path, and a wireless local area network data path. The satellite data path may include one or more of a low earth orbit satellite data path, a medium earth orbit satellite data path, and a geostationary earth orbit satellite data path. The first data path may include a medium earth orbit satellite data path or a low earth orbit satellite data path and the second data path may include a geostationary orbit satellite data path.
The method may further include, for each path of the number of data paths, maintaining an indication of successful and unsuccessful delivery of the messages over the data path and adjusting a congestion window for the data path based on the indication, which may occur under control of an expert system, including based on feedback of outcomes of a set of transmissions. The method may further include, for each path of the number of data paths, maintaining, at the first node, an indication of whether a number of messages received at the second node is sufficient to decode data associated with the messages, wherein the indication is based on feedback received at the first node over the number of data paths.
In another general aspect, a system for data communication between a number of nodes over a number of data paths coupling the number of nodes includes a first node configured to transmit messages to a second node over the number of data paths including transmitting a first subset of the messages over a first data path of the number of data paths, and transmitting a second subset of the messages over a second data path of the number of data paths.
In embodiments, the first subset of the messages and the second subset of the messages for the respective data paths may be determined from a portion of the messages available at a first node at a time of transmission. At a subsequent time of transmission, additional messages made available to the first node may be divided into a first subset and a second subset based on message characteristics associated with the additional messages. Messages having the first message characteristics may be associated with an initial subset of a data set and messages having the second message characteristics may be associated with a subsequent subset of the data set.
In embodiments, the messages of the second subset may include messages that are at most n messages ahead of a last acknowledged message in a sequential transmission order associated with the messages, wherein n is determined based on a receive buffer size at the second node. Messages having the first message characteristics may include acknowledgement messages and messages having the second message characteristics may include data messages. Messages having the first message characteristics may include supplemental data messages. The supplemental data messages may include data messages including redundancy data and messages having the second message characteristics may include original data messages.
The first node may be further configured to, for each path of the number of data paths, maintain an indication of successful and unsuccessful delivery of the messages over the data path and adjust a congestion window for the data path based on the indication. The first node may be further configured to maintain an aggregate indication of whether a number of messages received at the second node over the number of data paths is sufficient to decode data associated with the messages and to transmit supplemental messages based on the aggregate indication, wherein the aggregate indication is based on feedback from the second node received at the first node over the number of data paths.
1. A method for data communication between a first node and a second node over a plurality of data paths coupling the first node and the second node, the method comprising:
transmitting messages between the first node and the second node over the plurality of data paths including transmitting a first subset of the messages over a first data path of the plurality of data paths, and transmitting a second subset of the messages over a second data path of the plurality of data paths;
wherein the first data path has a first latency and the second data path has a second latency substantially larger than the first latency, and messages of the first subset of the messages are chosen to have first message characteristics and messages of the second subset are chosen to have second message characteristics, different from the first message characteristics, wherein the selection of the first and second subset of message characteristics is performed automatically under control of an expert system.
2. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the selection.
3. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to at least one of the data paths.
4. The method of clause 3 wherein the expert system takes a plurality of inputs from a data collector that accepts data about a machine operating in an industrial environment.
As described in US patent application 2017/0012868, entitled “Multiple protocol network communication,” self-organized network coding under control of an expert system may involve methods and systems for data communication between a first node and a second node over one or more data paths coupling the first node and the second node and may include transmitting messages between the first node and the second node over the data paths, including transmitting at least some of the messages over a first data path using a first communication protocol, transmitting at least some of the messages over a second data path using a second communication protocol, determining that the first data path is altering a flow of messages over the first data path due to the messages being transmitted using the first communication protocol, and in response to the determining, adjusting a number of messages sent over the data paths, including decreasing a number of the messages transmitted over the first data path and increasing a number of messages transmitted over the second data path. Determination that the first data path is altering a flow of messages and/or adjusting the number of messages sent over the data paths may occur under control of an expert system, such as a rule-based system, a model-based system, a machine learning system (including deep learning) or a hybrid of any of those, where the expert system takes inputs relating to one or more of the data paths, the nodes, the communication protocols used, or the like. The data paths may be among devices and systems in an industrial environment, such as instrumentation systems of industrial machines, one or more mobile data collectors (optionally coordinated in a swarm), data storage systems (including network-attached storage), servers and other information technology elements, any of which may have or be associated with one or more network nodes. The data paths may be among any such devices and systems and devices and systems in a network of any kind (such as switches, routers, and the like) or between those and ones located in a remote environment, such as in an enterprise's information technology system, in a cloud platform, or the like.
Determining that the first data path is altering the flow of messages over the first data path may include determining that the first data path is limiting a rate of messages transmitted using the first communication protocol. Determining that the first data path is altering the flow of messages over the first data path may include determining that the first data path is dropping messages transmitted using the first communication protocol at a higher rate than a rate at which the second data path is dropping messages transmitted using the second communication protocol. The first communication protocol may be the User Datagram Protocol (UDP), and the second communication protocol may be the Transmission Control Protocol (TCP), or vice versa. Other protocols as described throughout this disclosure may be used.
The messages may be initially equally divided or divided according to some predetermined allocation (such as by type, as noted in connection with other embodiments) across the first data path and the second data path, such as using a load balancing technique. The messages may be initially divided across the first data path and the second data path according to a division of the messages across the first data path and the second data path in one or more prior data communication connections. The messages may be initially divided across the first data path and the second data path based on a probability that the first data path will alter a flow of messages over the first data path due to the messages being transmitted using the first communication protocol.
The messages may be divided across the first data path and the second data path based on message type. The message type may include one or more of acknowledgement messages, forward error correction messages, retransmission messages, and original data messages. Decreasing a number of the messages transmitted over the first data path and increasing a number of messages transmitted over the second data path may include sending all of the messages over the second path and sending none of the messages over the first path.
At least some of the number of data paths may share a common physical data path. The first data path and the second data path may share a common physical data path. The adjusting of the number of messages sent over the number of data paths may occur during an initial phase of the transmission of the messages. The adjusting of the number of messages sent over the number of data paths may repeatedly occur over a duration of the transmission of the messages. The adjusting of the number of messages sent over the number of data paths may include increasing a number of the messages transmitted over the first data path and decreasing a number of messages transmitted over the second data path.
In some examples, the parallel transmission over TCP and UDP is handled differently from conventional load balancing techniques, because TCP and UDP both share a low-level data path and nevertheless have very different protocol characteristics.
In some examples, approaches respond to instantaneous network behavior and learn the network's data handling policy and state by probing for changes. In an industrial environment, this may include learning policies relating to authorization to use aspects of a network; for example, a SCADA system may allow a data path to be used only by a limited set of authorized users, services, or applications, because of the sensitivity of underlying machines or processes that are under control (including remote control) via the SCADA system and concern over potential for cyberattacks. Unlike conventional load-balancers which assume each data path is unique and does not affect the other, approaches may recognize that TCP and UDP share a low-level data path and directly affect each other. Additionally, TCP provides in-order delivery and retransmits data (along with flow control, congestion control, etc.) whereas UDP does not. This uniqueness requires additional logic provided by the methods and systems disclosed herein that may include mapping specific message types to each communication protocol, such as based at least in part on the different properties of the protocols (e.g. expect longer jitter over TCP, expect out-of-order delivery on UDP). For example, the system may refrain from coding over packets sent through TCP, since it is reliable, but may send forward error correction over UDP to add redundancy and save bandwidth. In some examples, a larger ACK interval is used for ACKing TCP data.
By employing the techniques described herein, approaches distribute data over TCP and UDP data paths to achieve optimal or near-optimal throughput, such as in situations where a network provider's policies treat UDP unfairly (as compared to conventional systems that simply use UDP if possible and fall back to TCP if not).
A method for data communication between a first node and a second node over a plurality of data paths coupling the first node and the second node, the method comprising:
transmitting messages between the first node and the second node over the plurality of data paths including transmitting at least some of the messages over a first data path of the plurality of data paths using a first communication protocol, and transmitting at least some of the messages over a second data path of the plurality of data paths using a second communication protocol;
determining that the first data path is altering a flow of messages over the first data path due to the messages being transmitted using the first communication protocol, and in response to the determining, adjusting a number of messages sent over the plurality of data paths including decreasing a number of the messages transmitted over the first data path and increasing a number of messages transmitted over the second data path, wherein altering the flow of messages is performed automatically under control of an expert system.
1. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the alteration of the flow.
2. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to at least one of the data paths.
3. The method of clause 3 wherein the expert system takes a plurality of inputs from a data collector that accepts data about a machine operating in an industrial environment.
4. The method of clause 1 wherein the first communication protocol is User Datagram Protocol (UDP).
5. The method of clause 1 wherein the second communication protocol is Transmission Control Protocol (TCP).
6. The method of clause 1 wherein the messages are initially divided across the first data path and the second data path using a load balancing technique.
7. The method of clause 1 wherein the messages are initially divided across the first data path and the second data path according to a division of the messages across the first data path and the second data path in one or more prior data communication connections.
8. The method of clause 1 wherein the messages are initially divided across the first data path and the second data path based on a probability that the first data path will alter a flow of messages over the first data path due to the messages being transmitted using the first communication protocol.
9. The method of clause 9, wherein the probability is determined by an expert system.
As described in US patent application 2017/0012884, entitled “Message reordering timers,” self-organized network coding under control of an expert system may involve methods and systems for data communication from a first node to a second node over a data channel coupling the first node and the second node and may include receiving data messages at the second node, the messages belonging to a set of data messages transmitted in a sequential order from the first node, sending feedback messages from the second node to the first node, the feedback messages characterizing a delivery status of the set of data messages at the second node, including maintaining a set of one or more timers according to occurrences of a number of delivery order events, the maintaining including modifying a status of one or more timers of the set of timers based on occurrences of the number of delivery order events, and deferring sending of said feedback messages until expiry of one or more of the set of one or more timers. The data channels may be among devices and systems in an industrial environment, such as instrumentation systems of industrial machines, one or more mobile data collectors (optionally coordinated in a swarm), data storage systems (including network-attached storage), servers and other information technology elements, any of which may have or be associated with one or more network nodes. The data channels may be among any such devices and systems and devices and systems in a network of any kind (such as switches, routers, and the like) or between those and ones located in a remote environment, such as in an enterprise's information technology system, in a cloud platform, or the like. Determination that that timers are required, configuration of timers, and initiation of the user of timers may occur under control of an expert system, such as a rule-based system, a model-based system, a machine learning system (including deep learning) or a hybrid of any of those, where the expert system takes inputs relating to one or more of the types of communications occurring, the data channels, the nodes, the communication protocols used, or the like.
The set of one or more timers may include a first timer and the first timer may be started upon detection of a first delivery order event, the first delivery order event being associated with receipt of a first data message associated with a first position in the sequential order prior to receipt of one or more missing messages associated with positions preceding the first position in the sequential order. The method may include sending the feedback messages indicating a successful delivery of the set of data messages at the second node upon detection of a second delivery order event, the second delivery order event being associated with receipt of the one or more missing messages prior to expiry of the first timer. The method may include sending said feedback messages indicating an unsuccessful delivery of the set of data messages at the second node upon expiry of the first timer prior to any of the one or more missing messages being received. The set of one or more timers may include a second timer and the second timer is started upon detection of a second delivery order event, the second delivery order event being associated with receipt of some but not all of the missing messages prior to expiry of the first timer. The method may include sending feedback messages indicating an unsuccessful delivery of the set of data messages at the second node upon expiry of the second timer prior to receipt of the missing messages. The method may include sending feedback messages indicating a successful delivery of the set of data messages at the second node upon detection of a third delivery order event, the third delivery order event being associated with receipt of the missing messages prior to expiry of the second timer.
In another general aspect, a method for data communication from a first node to a second node over a data channel coupling the first node and the second node includes receiving, at the first node, feedback messages indicative of a delivery status of a set of data messages transmitted in a sequential order to the second node from the second node, maintaining a size of a congestion window at the first node including maintaining a set of one or more timers according to occurrences of a number of feedback events, the maintaining including modifying a status of one or more timers of the set of timers based on occurrences of the number of feedback events, and delaying modification of the size of the congestion window until expiry of one or more of the set of one or more timers.
The set of one or more timers may include a first timer and the first timer may be started upon detection of a first feedback event, the first feedback event being associated with receipt of a first feedback message indicating successful delivery of a first data message having first position in the sequential order prior to receipt of one or more feedback messages indicating successful delivery of one or more other data messages having positions preceding the first position in the sequential order. The method may include cancelling modification of the congestion window upon detection of a second feedback event, the second feedback event being associated with receipt of one or more feedback messages indicating successful delivery of the one or more other data messages prior to expiry of the first timer. The method may include modifying the congestion window upon expiry of the first timer prior to receipt of any feedback message indicating successful delivery of the one or more other data messages.
The set of one or more timers may include a second timer and the second timer may be started upon detection of a third feedback event, the third feedback event being associated with receipt of one or more feedback messages indicating successful delivery of some but not all of the one or more other data messages prior to expiry of the first timer. The method may include modifying the size of the congestion window upon expiry of the second timer prior to receipt of one or more feedback messages indicating successful delivery of the one or more other data messages. The method may include cancelling modification of the size of the congestion window upon detection of a fourth feedback event, the fourth feedback event being associated with receipt one or more feedback messages indicating successful delivery of the one or more other data messages prior to expiry of the second timer.
In another general aspect, a system for data communication between a number of nodes over a data channel coupling the number of nodes includes a first node of the number of nodes configured to receive, at the first node, feedback messages indicative of a delivery status of a set of data messages transmitted in a sequential order to the second node from the second node, maintain a size of a congestion window at the first node including maintaining a set of one or more timers according to occurrences of a number of feedback events, the maintaining including modifying a status of one or more timers of the set of timers based on occurrences of the number of feedback events, and delaying modification of the size of the congestion window until expiry of one or more of the set of one or more timers.
1. A method for data communication from a first node to a second node over a data channel coupling the first node and the second node, the method comprising:
determining, using an expert system, based on at least one condition of the data channel, whether one or more timers will used to manage the data communication and, upon such determination:
receiving data messages at the second node, the messages belonging to a set of data messages transmitted in a sequential order from the first node;
sending feedback messages from the second node to the first node, the feedback messages characterizing a delivery status of the set of data messages at the second node, including
maintaining a set of one or more timers according to occurrences of a plurality of delivery order events, the maintaining including modifying a status of one or more timers of the set of timers based on occurrences of the plurality of delivery order events, and
deferring sending of said feedback messages until expiry of one or more of the set of one or more timers.
2. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the determination whether to use one or more timers.
3. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to at least one of the data paths.
4. The method of clause 3 wherein the expert system takes a plurality of inputs from a data collector that accepts data about a machine operating in an industrial environment.
5. The method of clause 1 wherein the set of one or more timers includes a first timer and the first timer is started upon detection of a first delivery order event, the first delivery order event being associated with receipt of a first data message associated with a first position in the sequential order prior to receipt of one or more missing messages associated with positions preceding the first position in the sequential order.
As described in US patent application 2017/0012885, entitled “Network communication recoding node,” self-organized network coding under control of an expert system may involve methods and systems for modifying redundancy information associated with encoded data passing from a first node to a second node over data paths and may include receiving first encoded data including first redundancy information at an intermediate node from the first node via a first channel connecting the first node and the intermediate node, the first channel having first channel characteristics, and transmitting second encoded data including second redundancy information from the intermediate node to the second node via a second channel connecting the intermediate node and the second node, the second channel having second channel characteristics. A degree of redundancy associated with the second redundancy information may be determined by modifying the first redundancy information based on one or both of the first channel characteristics and the second channel characteristics without decoding the first encoded data. The data paths may be among devices and systems in an industrial environment (each acting as one or more nodes for sending, receiving, or transmitting data), such as instrumentation systems of industrial machines, one or more mobile data collectors (optionally coordinated in a swarm), data storage systems (including network-attached storage), servers and other information technology elements, any of which may have or be associated with one or more network nodes. The data paths may be among any such devices and systems and devices and systems in a network of any kind (such as switches, routers, and the like) or between those and ones located in a remote environment, such as in an enterprise's information technology system, in a cloud platform, or the like. Modifying the redundancy information may occur by or under control of an expert system, such as a rule-based system, a model-based system, a machine learning system (including deep learning) or a hybrid of any of those, where the expert system takes inputs relating to one or more of the data paths, the nodes, the communication protocols used, or the like. Redundancy may result from (and may be identified at least in part based on), the combination or multiplexing of data from a set of data inputs, such as described throughout this disclosure.
Modifying the first redundancy information may include adding redundancy information to the first redundancy information. Modifying the first redundancy information may include removing redundancy information from the first redundancy information. The second redundancy information may be further formed by modifying the first redundancy information based on feedback from the second node indicative of successful or unsuccessful delivery of the encoded data to the second node. The first encoded data and the second encoded data may be encoded, such as using a random linear network code or a substantially random linear network code. Modifying the first redundancy information based on one or both of the first channel characteristics and the second channel characteristics may include modifying the first redundancy information based on one or more of a block size, a congestion window size, and a pacing rate associated with the first channel characteristics and/or the second channel characteristics.
The method may include sending a feedback message from the intermediate node to the first node acknowledging receipt of one or more messages at the intermediate node. The method may include receiving a feedback message from the second node at the intermediate node and, in response to receiving the feedback message, transmitting additional redundancy information to the second node.
In another general aspect, a system for modifying redundancy information associated with encoded data passing from a first node to a second node over a number of data paths includes an intermediate node configured to receive first encoded data including first redundancy information from the first node via a first channel connecting the first node and the intermediate node, the first channel having first channel characteristics and transmit second encoded data including second redundancy information from the intermediate node to the second node via a second channel connecting the intermediate node and the second node, the second channel having second channel characteristics. A degree of redundancy associated with the second redundancy information is determined by modifying the first redundancy information based on one or both of the first channel characteristics and the second channel characteristics without decoding the first encoded data.
1. A method for modifying redundancy information associated with encoded data passing from a first node to a second node over a plurality of data paths, the method comprising:
receiving first encoded data including first redundancy information at an intermediate node from the first node via a first channel connecting the first node and the intermediate node, the first channel having first channel characteristics;
transmitting second encoded data including second redundancy information from the intermediate node to the second node via a second channel connecting the intermediate node and the second node, the second channel having second channel characteristics;
wherein a degree of redundancy associated with the second redundancy information is determined by modifying the first redundancy information based on one or both of the first channel characteristics and the second channel characteristics without decoding the first encoded data, including modifying the first redundancy information based on one or more of a block size, a congestion window size, and a pacing rate associated with the first channel characteristics and/or the second channel characteristics, wherein modifying the first redundancy information occurs under control of an expert system.
2. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the modification of the redundancy information.
3. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to at least one of the data paths.
4. The method of clause 3 wherein the expert system takes a plurality of inputs from a data collector that accepts data about a machine operating in an industrial environment.
5. The method of clause 1 wherein modifying the first redundancy information includes adding redundancy information to the first redundancy information.
6. The method of clause 1 wherein modifying the first redundancy information includes removing redundancy information from the first redundancy information.
7. The method of clause 1 wherein the second redundancy information is further formed by modifying the first redundancy information based on feedback from the second node indicative of successful or unsuccessful delivery of the encoded data to the second node.
8. The method of clause 1 wherein the first encoded data and the second encoded data are encoded using a random linear network code.
As described in US patent application 2017/0012905, entitled “Error correction optimization,” self-organized network coding under control of an expert system may involve methods and systems for data communication between a first node and a second node over a data path coupling the first node and the second node and may include transmitting a segment of data from the first node to the second node over the data path as a number of messages, the number of messages being transmitted according to a transmission order. A degree of redundancy associated with each message of the number of messages is determined based on a position of said message in the transmission order. The data paths may be among devices and systems in an industrial environment (each acting as one or more nodes for sending, receiving, or transmitting data), such as instrumentation systems of industrial machines, one or more mobile data collectors (optionally coordinated in a swarm), data storage systems (including network-attached storage), servers and other information technology elements, any of which may have or be associated with one or more network nodes. The data paths may be among any such devices and systems and devices and systems in a network of any kind (such as switches, routers, and the like) or between those and ones located in a remote environment, such as in an enterprise's information technology system, in a cloud platform, or the like. Determining a transmission order may occur by or under control of an expert system, such as a rule-based system, a model-based system, a machine learning system (including deep learning) or a hybrid of any of those, where the expert system takes inputs relating to one or more of the data paths, the nodes, the communication protocols used, or the like. Redundancy may result from (and may be identified at least in part based on), the combination or multiplexing of data from a set of data inputs, such as described throughout this disclosure.
The degree of redundancy associated with each message of the number of messages may increase as the position of the message in the transmission order is non-decreasing. Determining the degree of redundancy associated with each message of the number of messages based on the position (i) of the message in the transmission order is further based on one or more of delay requirements for an application at the second node, a round trip time associated with the data path, a smoothed loss rate (P) associated with the channel, a size (N) of the data associated with the number of messages, a number (ai) of acknowledgement messages received from the second node corresponding to messages from the number of messages, a number (fi) of in-flight messages of the number of messages, and an increasing function (g(i)) based on the index of the data associated with the number of messages.
The degree of redundancy associated with each message of the number of messages may be defined as: (N+g(i)−ai)/(1−p)−fi. g(i) may be defined as a maximum of a parameter m and N−i. g(i) may be defined as N−p(i) where p is a polynomial, with integer rounding as needed. The method may include receiving, at the first node, a feedback message from the second node indicating a missing message at the second node and, in response to receiving the feedback message, sending a redundancy message to the second node to increase a degree of redundancy associated with the missing message. The method may include maintaining, at the first node, a queue of preemptively computed redundancy messages and, in response to receiving the feedback message, removing some or all of the preemptively computed redundancy messages from the queue and adding the redundancy message to the queue for transmission. The redundancy message may be generated and sent on-the-fly in response to receipt of the feedback message.
The method may include maintaining, at the first node, a queue of preemptively computed redundancy messages for the number of messages and, in response to receiving a feedback message indicating successful delivery of the number of messages, removing any preemptively computed redundancy messages associated with the number of messages from the queue of preemptively computed redundancy messages. The degree of redundancy associated with each of the messages may characterize a probability of correctability of an erasure of the message. The probability of correctability may depend on a comparison of between the degree of redundancy and a loss probability.
1. A method for data communication between a first node and a second node over a data path coupling the first node and the second node, the method comprising:
transmitting a segment of data from the first node to the second node over the data path as a plurality of messages, the plurality of messages being transmitted according to a transmission order;
wherein a degree of redundancy associated with each message of the plurality of messages is determined based on a position of said message in the transmission order, wherein the transmission order is determined under control of an expert system.
2. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the transmission order.
3. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to at least one of the data paths.
4. The method of clause 3 wherein the expert system takes a plurality of inputs from a data collector that accepts data about a machine operating in an industrial environment.
5. The method of clause 1 wherein the degree of redundancy associated with each message of the plurality of messages increases as the position of the message in the transmission order is non-decreasing.
6. The method of clause 1 wherein determining the degree of redundancy associated with each message of the plurality of messages based on the position (i) of the message in the transmission order is further based on one or more of:
application delay requirements;
a round trip time associated with the data path,
a smoothed loss rate (P) associated with the channel,
a size (N) of the data associated with the plurality of messages,
a number (ai) of acknowledgement messages received from the second node corresponding to messages from the plurality of messages,
a number (fi) of in-flight messages of the plurality of messages, and
an increasing function (g(i)) based on the index of the data associated with the plurality of messages.
As described in U.S. patent application Ser. No. 14/935,885, entitled, “Packet Coding Based Network Communication,” self-organized network coding under control of an expert system may involve methods and systems for data communication between a first node and a second node over a path and may include estimating a rate at which loss events occur, where a loss event is either an unsuccessful delivery of a single packet to the second data node or an unsuccessful delivery of a plurality of consecutively transmitted packets to the second data node, and sending redundancy messages at the estimated rate at which loss events occur. An expert system may be used to estimate the rate at which loss events occur.
A method for data communication from a first node to a second node over a data channel coupling the first node and the second node such as in an industrial environment, includes receiving messages at the first node, from the second node, including receiving messages comprising data that depend at least in part of characteristics of the channel coupling the first node and the second node, transmitting messages from the first node to the second node, including applying forward error correction according to parameters determined from the received messages, the parameters determined from the received messages including at least two of a block size, an interleaving factor, and a code rate. The method may occur under control of an expert system.
1. A method for data communication from a first node in an industrial environment to a second node over a data channel coupling the first node and the second node, the method comprising:
receiving messages at the first node from the second node, including receiving messages comprising data that depend at least in part of characteristics of the channel coupling the first node and the second node;
transmitting messages from the first node to the second node, including applying error correction according to parameters determined from the received messages, the parameters determined from the received messages including at least two of a block size, an interleaving factor, and a code rate, wherein applying the error correction occurs under control of an expert system.
2. The method of clause 1 wherein the expert system uses at least one of a rule and a model to set a parameter of the error correction.
3. The method of clause 1 wherein the expert system is a machine learning system that iteratively configures at least one of a set of inputs, a set of weights, and a set of functions based on feedback relating to at least one of the data paths.
As depicted in
Within the cloud platform 13000, various components may be deployed in a wide range of architectures and arrangements. In embodiments, devices 13006 may connect to, integrate with, or be deployed within a cloud computing environment 13068, the policy automation engine 13002, the data marketplace 13008, the data collectors 13020, as well as systems and capabilities for self-organization 13012, machine learning 13014 and rights management 13016. Devices 13006 may connect to or integrate with the policy automation engine 13002, data marketplace 13008, data collectors 13020 and systems or capabilities for self-organization 13012, machine learning 13014 and rights management 13016, either directly or through the cloud computing environment 13068.
Devices 13006 may be IoT devices, including IoT devices, such as for collecting, exchanging and managing information relating to machines, personnel, equipment, infrastructure elements, components, parts, inventory, assets, and other features of a wide range of industrial environments, such as those described throughout this disclosure. Devices 13006 may also connect via various protocols 13004, such as networking protocols, streaming protocols, file transfer protocols, data transformation protocols, software operating system protocols, and the like. Devices may connect to the policy automation engine 13002, such as for executing policies that may be deployed within the cloud platform 13000, such as governing activities, permissions, rules, and the like within the platform 13000. Devices 13006 may also connect to data streams 13010 within the data marketplace 13008.
Data pools 13070 may connect to or integrate with the cloud computing environment 13068, data collectors 13020 and the data marketplace 13008, policy automation engine 13002, self organization 13012, machine learning 13014 and rights management 13016 capabilities. Data pools 13070 may be included within the cloud computing environment 30 or be external to the cloud computing environment 13068. As a result, connections to the data pools 13070 may be made directly to the data pools 13070, through cloud connections to the data pools 13070 or through a combination of direct and cloud connections to the data pools 13070. Data pools 13070 may also be included within the data marketplace 13008 or external to the data marketplace 13008.
Data pools 13070 may include a multiplexer (MUX) 13022 and also connect to self organization 13012, machine learning 13014 and rights management capabilities. The MUX 13022 may connect to sensors 13024, collect data from sensors 13024 and integrate data collected from sensors 13024 into a single set of data. In an exemplary and non-limiting embodiment, data pools 13070, data collectors 13020 and sensors 13024 may be included within an industrial environment 13018.
A policy automation engine 13002 and data marketplace 13008 may be used in a variety of industrial environments 13018. Industrial environments 13018 may include aerospace environments, agriculture environment, assembly line environments, automotive environments and chemical and pharmaceutical environments. Industrial environments 13018 may also include food processing environments, industrial component environments, mining environments, oil and gas environments, particularly oil and gas production environments, truck and car environments and the like.
Similarly, devices 13006 may include a variety of devices that may operate within the industrial environments or that may collect data with respect to other such devices. Among many examples, devices 13006 may include agitators, including turbine agitators, airframe control surface vibration devices, catalytic reactors and compressors. Devices 13006 may also include conveyors and lifters, disposal systems, drive trains, fans, irrigation systems and motors. Devices 13006 may also include pipelines, electric powertrains, production platforms, pumps, such as water pumps, robotic assembly systems, thermic heating systems, tracks, transmission systems and turbines. Devices 13006 may operate within a single industrial environment 13018 or multiple industrial environments 13018. For example, a pipeline device may operate within an oil and gas environment, while a catalytic reactor may operate in either an oil and gas production environment or a pharmaceutical environment.
The policy automation engine 13002 may be a cloud-based policy automation engine 13002. A policy automation engine 13002 may be used to create, deploy, and/or manage an interconnected set of policies 13030, rules 13028 and protocols 13004, such as policies relating to security, authorization, permissions and the like. For example, policies may govern what users, applications, services, systems, devices, or the like may access an IoT device, may read data from an IoT device, may subscribe to a stream from an IoT device, may write data to an IoT device, may establish a network connection with an IoT device, may provision an IoT device, may collaborate with an IoT device, or the like.
The policy automation engine 13002 may generate and manage policies 13030. The policy generation engine may be the centralized policy management system for the cloud platform 13000.
Policies 13030 generated and managed by the policy automation engine 13002 may deploy a large number of rules 13028 to permit access to and use of different aspects of IoT devices. Policies 13030 may include IoT device creation policies 13032, IoT device deployment policies 13034, IoT device management policies 13036 and the like. The policies 13030 may be communicated to devices 13006 through protocols 13004 or directly from the policy automation engine 13002.
For example, in an exemplary and non-limiting embodiment, the policy automation engine 13002 may manage policies 13030 and create protocols 13004 that specify and enforce roles 13026 and permissions 13074 for workers, related to how the workers may use data provided by IoT devices. Workers may be human workers or machine workers.
In additional exemplary and non-limiting embodiments, policies 13030 may be used to automate remediation processes. Remediation processes may be performed when a system is partially disabled, when equipment fails and when an entire system may be disabled. Remediation processes may include instructions to initiate system restarts, bypass or replace equipment, notify appropriate stakeholders of the condition and the like. The policy automation engine 13002 may also include policies 13030 that specify the roles 13026 and permissions cp108 required for users 13072 to initiate or otherwise act upon the remediation or other processes.
The policy automation engine 13002 may also specify and detect conditions. Conditions may determine when policies 13030 are distributed or otherwise acted upon. Conditions may include individual conditions, sets of conditions, independent conditions, interdependent conditions and the like.
In an exemplary and non-limiting embodiment of an independent condition, the policy automation engine 13002 may determine that the failure of a non-critical device 13006 does not require notification of the system operator. In an exemplary and non-limiting embodiment of an interdependent set of conditions, the policy automation engine 13002 may determine that the failure of two non-critical system devices 13006 does require notification of the system operator, as the failure of two non-critical system devices 13006 may be an early indicator of a possible system-wide failure.
As depicted in
Policies 13030 may provide input to rules 13028 and provide information related to how roles 13026, permissions cp108 and uses 130280 are defined. Policies 13030 may receive policy inputs 13048 and incorporate policy inputs 13048 as policy parameters that are included within policies 13030. Policies 13030 may provide inputs to protocols 13004 and be included within protocols 13004 that are used to create, deploy and manage devices 13006.
Compliance policies 13050 may include data ownership policies, data analysis policies, data use policies, data format policies, data transmission policies, data security policies, data privacy policies, information sharing policies, jurisdictional policies and the like. Data transmission policies may include cross jurisdictional data transmission policies.
Data ownership policies may indicate policies 13030 that manage who controls data, who can use data, how the data can be used and the like. Data analysis policies may indicate what data holders can do with data that they are permitted to access, as well as determine what data they can look at and what data may be combined with other data. For example a data holder may look at aggregated user data but not individual user data. Data use policies may indicate how data may be used and under what circumstances data may be used.
Data format policies may indicate standard formats and mandated formats permitted for the handling of data. Data transmission policies, including cross jurisdictional data transmission policies, may determine the policies 13030 that specify how inter-jurisdictional and intra jurisdictional transmission of data may be handled Data security policies may determine how data at rest, for example stored data, as well transmitted data is required to be secured.
Data privacy policies may determine how data may or may not be shared, for example within an organization and external to an organization. Information sharing policies may determine how data may be sold, shared and under what circumstances information can be sold and shared. Jurisdictional policies may determine who controls data, when and where the data may be controlled, for data within and transmitted across boundaries.
FCAPS policies 13052 may include fault management policies, configuration management policies, accounting management policies, provisioning management policies, and security management policies. Fault management policies may specify policies 13030 used to handle device faults. Configuration management policies may specify policies used to configure devices 13006. Accounting management policies may specify policies 13030 used for device accounting purposes, such as reporting, billing and the like. Provisioning management policies may specify policies 13030 used to provision services on devices 13006. Security management policies may specify policies 13030 used to secure devices 13006.
Policy inputs 13048 may be received from a policy input interface 13046. Policy inputs 13048 may include standards-based policy inputs 13044 and other policy inputs 13048. Standards-based policy inputs 13044 may include inputs related to standard data formats, standard rule sets and other standards-related information set by standards bodies, for example.
Other policy inputs 13048 may include a wide range of information related industry-specific policies, cross-industry policies, manufacturer-specific policies, device-specific policies 13030 and the like. Policy inputs 13048 may connect to a cloud cloud computing environment 13068 and be provided through a policy input interface 13046. The policy input interface 13046 may collect policy inputs 13048 provided by machines or entered by human operators.
As depicted in
The data marketplace 13008 may connect to data pools 13070 directly, for example if the data marketplace 13008 and data pools 13070 are located in the same physical location. The data marketplace 13008 may connect to data pools 13070 via a cloud networking environment 30, for example if the data marketplace 13008 and data pools 13070 are located in different physical locations.
The data marketplace 13008 may connect to and receive inputs. The data marketplace 13008 may receive marketplace inputs through data interfaces, for example one or more data collectors 13020. The data collectors 13020 may be multiplexing data collectors. Inputs received through the data collectors 13020 may be received as one or more than one data streams 13010 from one or more than one data collectors 13020 and integrated into additional data streams 13010 by the multiplexer (MUX) 13022.
The data streams 13010 may also include data from the data pools 60. Data marketplace inputs CP162, data streams 13010 and data pools 13070 may include metrics and measures of success of the data marketplace 13008. The metrics and measures of success of the data marketplace 13008 may then be used by the machine learning capability 13014 to configure one or more parameters of the data marketplace 13008.
Inputs may be consortia inputs 13054. Consortia inputs 13054 may be received from consortia. Consortia may include energy consortia, healthcare consortia, manufacturing consortia, smart city consortia, transportation consortia and the like. Consortia may be pre-existing consortia or new consortia.
In an exemplary and non-limiting embodiment, new consortia may be formed as a result of the data marketplace 13008 making available particular data types and data combinations. The data brokering engine 13042 may allow consortia members to trade information. The data brokering engine 13042 may allow consortia members to trade information based on information value, as calculated by the marketplace value rating engine 13040, for example.
The data marketplace 13008 may also connect to self organization 13012, machine learning 13014 and rights management 13016 capabilities. Rights management capabilities 13016 may include rights.
Rights may include business strategy and solution rights, liaison rights 13058, marketing rights 13078, security rights 13060, technology rights 13062, testbed rights 13064 and the like. Business strategy and solution lifecycle rights may include business strategy and planning rights, industrial internet system design rights, project management rights, solution evaluation and contractual aspects rights. Liaison rights 13058 may include standards organization rights, open-source community rights, certification and testing body rights and governmental organization rights. Marketing rights 13078 may include communication rights, energy rights, healthcare rights, marketing-security rights, retail operation rights, smart factory rights and thought leadership rights. Security rights 13060 may include driving rights that drive industry consensus, promote security best practices and accelerate the adoption of security best practices.
Technology rights 13062 may include architecture rights, connectivity rights, distributed data management and interoperability rights, industrial analytics rights, innovation rights, IT/OT rights, safety rights, vocabulary rights, use case rights and liaison rights 13058. Testbed rights 13064 may include rights to implement of specific use cases and scenarios, as well as rights to produce testable outcomes to confirm that an implementation conforms to expected results, for example. Testbed rights 13064 may also include rights to explore untested or existing technologies working together, for example interoperability testing, generate new and potentially disruptive products and services and generate requirements and priorities for standards organizations, consortia and other stakeholder groups.
The rights management capability may assign different rights to different participants in the data marketplace 13008. In an exemplary and non-limiting embodiment, manufacturers or remote maintenance organizations (RMOs). Participants may be assigned rights to information based on their equipment or proprietary methods. The data marketplace 13008 may then ensure that only the appropriate data streams 13010 are made available to the market, based on the assigned rights.
The rights management capability 13016 may manage permissions to access the data in the marketplace 13008. One or more parameters of the rights management capability 13016 may be automatically configured by the machine learning capability 13014 and may be based on a metric of success of the data marketplace 13008. The machine learning engine 13014 may also use the metric and measure of success to configure a user interface. The user interface may present a data element of the user of the data marketplace 13008. The user interface may also present one or more mechanisms by which a user of the data marketplace 13008 may obtain access to one or more of the data elements.
The data payment allocation engine 13038 may allocate data marketplace payments. The data payment allocation engine 13038 may allocate data marketplace payments according to the value of a data stream 13010, the value of a contribution to a data stream 13010 and the like. This type of payment allocation may allow the data marketplace 13008 to allocate payments to data contributors, based on the value of the data contributions.
For example, contributors of data to a higher-value data stream 13010 may receive higher payments than contributors of data to lower-value data streams 13010. Similarly, data marketplace participants, for example IoT device manufacturers and system integrators, may be rated or ranked by the value of the data or the power of the configurations they provide and support.
The data marketplace 13008 may be a self-organizing data marketplace. A self organizing data marketplace may self-organize using self-organization capabilities 13012. Self-organization capabilities 13012 may be learned, developed and optimized using artificial intelligence (AI) capabilities. AI capabilities may be provided by the machine learning 13014 capability, for example. Self-organization may occur via an expert system and may be based on the application of a model, one or more rules, or the like. Self-organization may occur via a neural network or deep learning system, such as by optimizing variations of the organization of the data pool over time based on feedback to one or more measures of success. Self-organization may occur by a hybrid or combination of a rule-based system, model-based system, and neural network or other AI system. Various capabilities may be self-organized, such as how data elements are presented in the user interface of the marketplace, what data elements are presented, what data streams are obtained as inputs to the marketplace, how data elements are described, what metadata is provided with data elements, how data elements are stored (such as in a cache or other “hot” storage or in slower, but less expensive storage locations), where data elements are stored (such as in edge elements of a network), how data elements are combined, fused or multiplexed, or the like. Feedback to self-organization may include various metrics and measures of success, such as profit measures, yield measures, ratings (such as by users, purchasers, licensees, reviewers, and the like), indicators of interest (such as clickstream activity, time spent on a page, time spent reviewing elements and links to data elements), and others as described throughout this disclosure.
Data marketplace inputs 13056, data streams 13010 and data pools 13070 may be organized, based on metrics and measures of success of the data marketplace 13008. Data marketplace inputs 13056, data streams 13010 and data pools 13070 may be organized by the self-organization capability 13012, allowing the marketplace inputs 13056, data streams 13010, and data pools to be organized automatically, without requiring interaction by a user of the data marketplace. 13008.
The metric and measure of success may also be used to configure the data brokering engine 13042 to execute a transaction among at least two marketplace participants. The machine learning engine 13014 may use the metric of success to configure the data brokering engine 13042 automatically, without requiring user intervention. The metric of success may also be used by a pricing engine, for example the marketplace value rating engine 13040, to set the price of one or more data elements within the data marketplace 13008.
In an exemplary and non-limiting embodiment, the self-organizing data marketplace may self-organize to determine which type of data streams 13010 are the most valuable and offer the most valuable and other data streams 13010 for sale. The calculation of data stream value may be performed by the marketplace value rating engine 13040.
1. A policy automation system for a data collection system in an industrial environment, comprising:
a policy input interface structured to receive policy inputs relating to definition of at least one parameter of at least one of a rule, a policy and a protocol, wherein the at least one parameter defines at least one of a configuration for a data collection device, an access policy for accessing data from the data collection device, and collection policy for collection of data by the device; and
a policy automation engine for taking the inputs and automatically configuring and deploying at least one of the rule, the policy and the protocol within the system for data collection.
Wherein the at least one parameter further defines at least one of an energy utilization policy, a cost-based policy, a data writing policy, and a data storage policy.
Wherein the parameter relates to a policy selected from among compliance, fault, configuration, accounting, provisioning and security policies for defining how devices are created, deployed and managed
Wherein the compliance policies include data ownership policies
Wherein the data ownership policies specify who owns data
Wherein the data ownership policies specify how owners may use data
Wherein the compliance policies include data analysis policies
Wherein the data analysis policies specify what data holders may access
Wherein the data analysis policies specify how data holders may use data
Wherein the data analysis policies specify how data may be combined with other data by data holders
Wherein the compliance policies include data use policies
Wherein the compliance policies include data format policies
Wherein the data format policies include standard data format policies
Wherein the data format policies include mandated data format policies
Wherein the compliance policies include data transmission policies
Wherein the data transmission policies include inter-jurisdictional transmission data transmission policies
Wherein the data transmission policies include inter-jurisdictional transmission data transmission policies
Wherein the compliance policies include data security policies
Wherein the data security policies include at rest data security policies
Wherein the data security policies include transmitted data security policies
Wherein the compliance policies include data privacy policies
Wherein the compliance policies include information sharing policies
Wherein the information sharing policies include policies specifying when information may be sold
Wherein the information sharing policies include policies specifying when information may be shared
Wherein the compliance policies include jurisdictional policies
Wherein the jurisdictional policies include policies specifying who controls data
Wherein the jurisdictional policies include policies specifying when data may be controlled
Wherein the jurisdictional policies include policies specifying how data transmitted across boundaries is controlled
2. A policy automation system for a data collection system in an industrial environment, comprising:
A policy automation engine for enabling configuration of a plurality of policies applicable to collection and utilization of data handled by a plurality of network connected devices deployed in a plurality of industrial environments, wherein the policy automation engine is hosted on information technology infrastructure elements that are located separately from the industrial environment, wherein upon configuration of a policy in the policy automation engine, the policy is automatically deployed across a plurality of devices in the plurality of industrial environments, wherein the policy sets configuration parameters relating to what data is collected by the data collection system and relating to access permissions for the collected data.
Wherein the policies include a plurality of policies selected among compliance, fault, configuration, accounting, provisioning and security policies for defining how devices are created, deployed and managed, and the plurality of policies communicatively coupled to policies
Further comprising a policy input interface structured to receive policy inputs used as an input to at least one of a rule, policy and protocol definition,
wherein the policy automation system a centralized source of policies for creating, deploying and managing policies for devices within an industrial environment.
3. A policy automation system for a data collection system in an industrial environment, comprising:
A policy automation engine for enabling configuration of a plurality of policies applicable to collection and utilization of data handled by a plurality of network connected devices deployed in a plurality of industrial environments, wherein the policy automation engine is hosted on information technology infrastructure elements that are located separately from the industrial environment, wherein upon configuration of a policy in the policy automation engine, the policy is automatically deployed across a plurality of devices in the plurality of industrial environments, wherein the policy sets configuration parameters relating to what data is collected by the data collection system and relating to access permissions for the collected data, wherein the policy automation system is communicatively coupled to a plurality of devices through a cloud network connection.
Wherein the cloud network connection is a privately-owned cloud connection.
Wherein the cloud network connection is a publicly provided cloud connection.
Wherein the cloud network connection is a publicly provided cloud connection.
Wherein the cloud network connection is the primary connection between the policy automation system and device.
Wherein the cloud network connection is the primary connection between the policy automation system and device.
Wherein the cloud network connection is an intranet cloud connection, connecting devices within a single enterprise.
Wherein the cloud network connection is an extranet cloud connection, connecting devices among multiple
enterprises.
Wherein the cloud network connection is a secure cloud network connection.
Wherein the secure cloud network connection is secured by a virtual private network (VPN) connection.
4. A system for data collection in an industrial environment having a self-organizing data marketplace for industrial IoT data.
5. A data marketplace for a data collection system in an industrial environment, comprising:
an input interface structured to receive marketplace inputs;
at least one of a data pool and a data stream to provide collected data within the marketplace and
data streams that include data from data pools.
Wherein at least one parameter of the marketplace is automatically configured by a machine learning facility based
on a metric of success of the marketplace.
Wherein the inputs include a plurality of data streams from a plurality of industrial data collectors.
Wherein the data collectors are multiplexing data collectors.
Wherein inputs include consortia inputs.
Wherein a consortium is an existing consortium.
Wherein a consortium is a consortium is related to a data stream through a common interest.
Wherein a consortium is a new consortium.
Wherein a consortium is a new consortium related to a data stream through a common interest.
Wherein the metrics and measures of success include profit measures.
Wherein the metrics and measures of success include yield measures.
Wherein the metrics and measures of success include ratings.
Wherein the ratings include user ratings.
Wherein the ratings include purchaser ratings.
Wherein the ratings include licensee ratings.
Wherein the ratings include reviewer ratings.
Wherein the metrics and measures success include indicators of interest.
Wherein the indicators of interest include clickstream activity.
Wherein the indicators of interest include time spent on a page.
Wherein the indicators of interest include time spent reviewing elements.
Wherein the indicators of interest include links to data elements.
6. A data marketplace for a data collection system in an industrial environment, comprising:
an input system structured to receive a plurality of data inputs relating to data sensed from or about one or more industrial machines;
at least one of a data pool and a data stream to provide collected data within the marketplace;
and a self-organization system for organizing at least one of the data inputs and the data pools based on a metric of success of the marketplace.
Wherein the self-organization system may optimize variations of the organization of the data pool over time.
Wherein the optimized variations may be based on feedback to one or more measures of success.
Wherein the self-organization system may organize how data elements are presented in the user interface of the marketplace.
Wherein the self-organization system selects what data elements are presented.
Wherein the self-organization system selects what data streams are obtained as inputs to the marketplace.
Wherein the self-organization system selects how data elements are described.
Wherein the data element description selects what metadata is provided with data elements.
Wherein the self-organization system selects a storage method for data elements. Wherein a storage method includes a cache or other “hot” storage method.
Wherein a storage method includes slower, but less expensive storage locations.
Wherein the self-organization system selects a location within a communication network for the storage elements (such as in edge elements of a network).
Wherein the self-organization system selects a data element combination method.
Wherein the data element combination method is a data fusion method.
Wherein the data element combination method is a data multiplexing method.
Wherein the self-organization system receives feedback data.
Wherein feedback data includes success metrics and measures.
Wherein success metrics and measures include profit measures.
Wherein success metrics and measures include yield measures.
Wherein success metrics and measures include ratings.
Wherein ratings include ratings provided by users.
Wherein ratings include ratings provided by purchasers.
Wherein ratings include ratings provided by licensees.
Wherein ratings include ratings provided by reviewers.
Wherein success metrics and measures include indicators of interest.
Wherein indicators of interest include clickstream activity.
Wherein indicators of interest include time spent on a page activity.
Wherein indicators of interest include time spent reviewing elements.
Wherein indicators of interest include time spent reviewing elements.
Wherein indicators of interest include links to data elements.
Wherein the self-organization system determines the value of data streams.
Wherein the value of data streams determines which data streams are offered for sale by the data marketplace.
Wherein the metrics and measures of success include profit measures.
Wherein the metrics and measures of success include yield measures.
Wherein the metrics and measures of success include ratings.
Wherein the ratings include user ratings.
Wherein the ratings include purchaser ratings.
Wherein the ratings include licensee ratings.
Wherein the ratings include reviewer ratings.
Wherein the metrics and measures success include indicators of interest.
Wherein the indicators of interest include clickstream activity.
Wherein the indicators of interest include time spent on a page.
Wherein the indicators of interest include time spent reviewing elements.
Wherein the indicators of interest include links to data elements.
7. A data marketplace for a data collection system in an industrial environment, comprising:
an input interface structured to receive data inputs from or about one or more of a plurality of industrial machines; at least one of a data pool and a data stream to provide collected data within the marketplace; and
a rights management engine for managing permissions to access the data in the marketplace.
Wherein at least one parameter of the rights management engine is automatically configured by a machine learning facility based on a metric of success of the marketplace.
wherein the rights management engine assigns rights to participants of the data marketplace.
Wherein the rights include business strategy and solution rights.
Wherein the rights include liaison rights.
Wherein the rights include marketing rights.
Wherein the rights include security rights.
Wherein the rights include technology rights.
Wherein the rights include testbed rights.
Wherein the metrics and measures of success include profit measures.
Wherein the metrics and measures of success include yield measures.
Wherein the metrics and measures of success include ratings.
Wherein the ratings include user ratings.
Wherein the ratings include purchaser ratings.
Wherein the ratings include licensee ratings.
Wherein the ratings include reviewer ratings.
Wherein the metrics and measures success include indicators of interest.
Wherein the indicators of interest include clickstream activity.
Wherein the indicators of interest include time spent on a page.
Wherein the indicators of interest include time spent reviewing elements.
Wherein the indicators of interest include links to data elements.
8. A data marketplace for a data collection system in an industrial environment, comprising:
an input interface structured to receive data inputs from or about one or more of a plurality of industrial machines; at least one of a data pool and a data stream to provide collected data within the marketplace; and a data brokering engine configured to execute a data transaction among at least two marketplace participants.
Wherein at least one parameter of the data brokering engine is automatically configured by a machine learning facility based on a metric of success of the marketplace.
Wherein a data transaction input includes a marketplace value rating.
Wherein a marketplace value rating is assigned to a marketplace participant.
Wherein a marketplace value rating assigned to a marketplace participant is assigned based on the value of input provided by the participant to the marketplace.
Wherein a data transaction is a trade transaction.
Wherein a data transaction is a sale transaction.
Wherein a data transaction is a payment transaction.
Wherein the metrics and measures of success include profit measures.
Wherein the metrics and measures of success include yield measures.
Wherein the metrics and measures of success include ratings.
Wherein the ratings include user ratings.
Wherein the ratings include purchaser ratings.
Wherein the ratings include licensee ratings.
Wherein the ratings include reviewer ratings.
Wherein the metrics and measures success include indicators of interest.
Wherein the indicators of interest include clickstream activity.
Wherein the indicators of interest include time spent on a page.
Wherein the indicators of interest include time spent reviewing elements.
Wherein the indicators of interest include links to data elements.
9. A data marketplace for a data collection system in an industrial environment, comprising:
an input interface structured to receive data inputs from or about one or more of a plurality of industrial machines; at least one of a data pool and a data stream to provide collected data within the marketplace; and
a pricing engine for setting a price for at least one data element within the marketplace.
Wherein pricing is automatically configured for the pricing engine by a machine learning facility based on a metric of success of the marketplace.
Wherein the metrics and measures of success include profit measures.
Wherein the metrics and measures of success include yield measures.
Wherein the metrics and measures of success include ratings.
Wherein the ratings include user ratings.
Wherein the ratings include purchaser ratings.
Wherein the ratings include licensee ratings.
Wherein the ratings include reviewer ratings.
Wherein the metrics and measures success include indicators of interest.
Wherein the indicators of interest include clickstream activity.
Wherein the indicators of interest include time spent on a page.
Wherein the indicators of interest include time spent reviewing elements.
Wherein the indicators of interest include links to data elements.
10. A data marketplace for a data collection system in an industrial environment, comprising:
an input interface structured to receive data inputs from or about one or more of a plurality of industrial machines; at least one of a data pool and a data stream to provide collected data within the marketplace; and
a user interface for presenting a data element and at least one mechanism by which a party using the marketplace can obtain access to the at least one data stream or data pool.
Wherein the user interface is automatically configured by a machine learning facility based on a metric of success of the marketplace.
Wherein the metrics and measures of success include profit measures.
Wherein the metrics and measures of success include yield measures.
Wherein the metrics and measures of success include ratings.
Wherein the ratings include user ratings.
Wherein the ratings include purchaser ratings.
Wherein the ratings include licensee ratings.
Wherein the ratings include reviewer ratings.
Wherein the metrics and measures success include indicators of interest.
Wherein the indicators of interest include clickstream activity.
Wherein the indicators of interest include time spent on a page.
Wherein the indicators of interest include time spent reviewing elements.
Wherein the indicators of interest include links to data elements.
11. A data collection system in an industrial environment, comprising:
A policy automation system for a data collection system in an industrial environment, comprising:
a plurality of rules selected among roles, permissions and uses, the plurality of rules communicatively coupled to policies, protocols and policy inputs;
a plurality of policies selected among compliance, fault, configuration, accounting, provisioning and security policies for defining how devices are created, deployed and managed, the plurality of policies communicatively coupled to policies, protocols and policy inputs and
a policy input interface structured to receive policy inputs used as an input to at least one of a rule, policy and protocol definition; and
12. A data marketplace comprising:
an input interface structured to receive marketplace inputs;
a plurality of data pools to store collected data, including marketplace inputs and make collected data available for use by the marketplace; and
data streams that include data from data pools.
As described herein and in Appendix B attached hereto, intelligent industrial equipment and systems may be configured in various networks, including self-forming networks, private networks, Internet-based networks, and the like. One or more of the smart heating systems as described in Appendix B that may incorporate hydrogen production, storage, and use may be configured as nodes in such a network. In embodiments, a smart heating system may be configured with one or more network ports, such as a wireless network port that facilitate connection through WiFi and other wired and/or wireless communication protocols as described. The smart heating system includes a smart hydrogen production system and a smart hydrogen storage system, and the like described in Appendix B and may be configured individually or as an integral system connected as one or more nodes in a network of industrial equipment and systems. By way of this example, a smart heating system may be disposed in an on-site industrial equipment operations center, such as a portable trailer equipped with communication capabilities and the like. Such deployed smart heating system may be configured, manually, automatically, or semi-automatically to join a network of devices, such as industrial data collection, control, and monitoring nodes and participate in network management, communication, data collection, data monitoring, control, and the like.
In another example of a smart heating system participating in a network of industrial equipment monitoring, control, and data collection devices in that a plurality of the smart heating systems may be configured into a smart heating system sub-network. In embodiments, data generated by the sub-network of devices may be communicated over the network of industrial equipment using the methods and systems described herein.
In embodiments, the smart heating system may participate in a network of industrial equipment as described herein. By way of this example, one or more of the smart heating systems, as depicted in
In embodiments, one or more smart heating systems described in Appendix B may incorporate, integrate, use, or connect with facilities, platforms, modules, and the like that may enable the smart heating system to perform functions such as analytics, self-organizing storage, data collection and the like that may improve data collection, deploy increased intelligence, and the like. Various data analysis techniques, such as machine pattern recognition of data, collection, generation, storage, and communication of fusion data from analog industrial sensors, multi-sensor data collection and multiplexing, self-organizing data pools, self-organizing swarm of industrial data collectors, and others described herein may be embodied in, enabled by, used in combination with, and derived from data collected by one or more of the smart heating systems.
In embodiments, a smart heating system may be configured with local data collection capabilities for obtaining long blocks of data (i.e., long duration of data acquisition), such as from a plurality of sensors, at a single relatively high-sampling rate as opposed to multiple sets of data taken at different sampling rates. By way of this example, the local data collection capabilities may include planning data acquisition routes based on historical templates and the like. In embodiments, the local data collection capabilities may include managing data collection bands, such as bands that define a specific frequency band and at least one of a group of spectral peaks, true-peak level, crest factor and the like.
In embodiments, one or more smart heating systems may participate as a self organizing swarm of IoT devices that may facilitate industrial data collection. The smart heating systems may organize with other smart heating systems, IoT devices, industrial data collectors, and the like to organize among themselves to optimize data collection based on the capabilities and conditions of the smart heating system and needs to sense, record, and acquire information from and around the smart heating systems. In embodiments, one or more smart heating systems may be configured with processing intelligence and capabilities that may facilitate coordinating with other members, devices, or the like of the swarm. In embodiments, a smart heating system member of the swarm may track information about what other smart heating systems in a swarm are handling and collecting to facilitate allocating data collection activities, data storage, data processing and data publishing among the swarm members.
In embodiments, a plurality of smart heating systems may be configured with distinct burners but may share a common hydrogen production system and/or a common hydrogen storage system. In embodiments, the plurality of smart heating systems may coordinate data collection associated with the common hydrogen production and/or storage systems so that data collection is not unnecessarily duplicated by multiple smart heating systems. In embodiments, a smart heating system that may be consuming hydrogen may perform the hydrogen production and/or storage data collection so that as smart heating system may prepare to consume hydrogen, they coordinate with other smart heating systems to ensure that their consumption is tracked, even if another smart heating system performs the data collection, handling, and the like. In embodiments, smart heating systems in a swarm may communicate among each other to determine which smart heating system will perform hydrogen consumption data collection and processing when each smart heating system prepares to stop consumption of hydrogen, such as when heating, cooking, or other use of the heat is nearing completion and the like. By way of this example when a plurality of smart heating systems is actively consuming hydrogen, data collection may be performed by a first smart heating system, data analytics may be performed by a second smart heating system, and data and data analytics recording or reporting may be performed by a third smart heating system. By allocating certain data collection, processing, storage, and reporting functions to different smart heating systems, certain smart heating systems with sufficient storage, processing bandwidth, communication bandwidth, available energy supply and the like may be allocated an appropriate role. When a smart heating system is nearing an end of its heating time, cooking time, or the like, it may signal to the swarm that it will be going into power conservation mode soon and, therefore, it may not be allocated to perform data analysis or the like that would need to be interrupted by the power conservation mode.
In embodiments, another benefit of using a swarm of smart heating systems as disclosed herein is that data storage capabilities of the swarm may be utilized to store more information than could be stored on a single smart heating system by sharing the role of storing data for the swarm.
In embodiments, the self-organizing swarm of smart heating systems includes one of the systems being designated as a master swarm participant that may facilitate decision making regarding the allocation of resources of the individual smart heating systems in the swarm for data collection, processing, storage, reporting and the like activities.
In embodiments, the methods and systems of self-organizing swarm of industrial data collectors may include a plurality of additional functions, capabilities, features, operating modes, and the like described herein. In embodiments, a smart heating system may be configured to perform any or all of these additional features, capabilities, functions, and the like without limitation.
The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and/or components as known in the art. The computing and/or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM, and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements. The methods and systems described herein may be configured for use with any kind of private, community, or hybrid cloud computing network or cloud computing environment, including those which involve features of software as a service (“SaaS”), platform as a service (“PaaS”), and/or infrastructure as a service (“IaaS”).
The methods, program codes, and instructions described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (“FDMA”) network or code division multiple access (“CDMA”) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like. The cell network may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.
The methods, program codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.
The computer software, program codes, and/or instructions may be stored and/or accessed on machine readable transitory and/or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (“RAM”); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read/write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.
The methods and systems described herein may transform physical and/or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and/or intangible items from one state to another.
The elements described and depicted herein, including in flow charts and block diagrams throughout the Figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable transitory and/or non-transitory media having a processor capable of executing program instructions stored thereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers, and the like. Furthermore, the elements depicted in the flow chart and block diagrams, or any other logical component, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and/or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context.
The methods and/or processes described above, and steps associated therewith, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and/or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.
The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.
Thus, in one aspect, methods described above, and combinations thereof, may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.
While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.
The use of the terms “a” and “an” and “the” and similar referents in the context of describing the disclosure (especially in the context of the following claims) is to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the disclosure, and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.
While the foregoing written description enables one skilled in the art to make and use what is considered presently to be the best mode thereof, those skilled in the art will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The disclosure should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the disclosure.
Any element in a claim that does not explicitly state “means for” performing a specified function, or “step for” performing a specified function, is not to be interpreted as a “means” or “step” clause as specified in 35 U.S.C. § 112(f). In particular, any use of “step of” in the claims is not intended to invoke the provision of 35 U.S.C. § 112(f).
Persons skilled in the art may appreciate that numerous design configurations may be possible to enjoy the functional benefits of the inventive systems. Thus, given the wide variety of configurations and arrangements of embodiments of the present invention, the scope of the invention is reflected by the breadth of the claims below rather than narrowed by the embodiments described above.
This application claims the benefit of U.S. Provisional Pat. App. No. 62/584,103, filed 9 Nov. 2017, entitled “Methods and Systems for the Industrial Internet of Things”. This application also is a bypass continuation-in-part of International Pat. App. No. PCT/US17/31721, filed on 9 May 2017, published on 16 Nov. 2017 as WO 2017/196821, and entitled “Methods and Systems for the Industrial Internet of Things”. International Pat. App. No. PCT/US17/31721 claims the benefit of: U.S. Provisional Pat. App. No. 62/333,589, filed 9 May 2016, entitled “Strong Force Industrial IoT Matrix”; U.S. Provisional Pat. App. No. 62/350,672, filed 15 Jun. 2016, entitled “Strategy for High Sampling Rate Digital Recording of Measurement Waveform Data as Part of an Automated Sequential List that Streams Long-Duration and Gap-Free Waveform Data to Storage for more flexible Post-Processing”; U.S. Provisional Pat. App. No. 62/412,843, filed 26 Oct. 2016, entitled “Methods and Systems for the Industrial Internet of Things”; and U.S. Provisional Pat. App. No. 62/427,141, filed 28 Nov. 2016, entitled “Methods and Systems for the Industrial Internet of Things”. All of the above applications are hereby incorporated by reference in their entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3706982 | Gehman | Dec 1972 | A |
| 3714822 | Lutz | Feb 1973 | A |
| 3731526 | Games | May 1973 | A |
| 3758764 | Harner | Sep 1973 | A |
| 4060716 | Pekrul et al. | Nov 1977 | A |
| 4074142 | Jackson | Feb 1978 | A |
| 4605928 | Georgiou | Aug 1986 | A |
| 4620304 | Faran et al. | Oct 1986 | A |
| 4665398 | Lynch et al. | May 1987 | A |
| 4740736 | Sidman et al. | Apr 1988 | A |
| 4852083 | Niehaus et al. | Jul 1989 | A |
| 4881071 | Monterosso et al. | Nov 1989 | A |
| 4945540 | Kaneko | Jul 1990 | A |
| 4980844 | Demjanenko et al. | Dec 1990 | A |
| 4985857 | Bajpai et al. | Jan 1991 | A |
| 4991429 | Stacey et al. | Feb 1991 | A |
| 5045851 | Davis et al. | Sep 1991 | A |
| 5072366 | Simcoe | Dec 1991 | A |
| 5123011 | Hein et al. | Jun 1992 | A |
| 5155802 | Mueller et al. | Oct 1992 | A |
| 5157629 | Sato et al. | Oct 1992 | A |
| 5182760 | Montgomery | Jan 1993 | A |
| 5276620 | Bottesch | Jan 1994 | A |
| 5311562 | Palusamy et al. | May 1994 | A |
| 5386373 | Keeler et al. | Jan 1995 | A |
| 5407265 | Hamidieh et al. | Apr 1995 | A |
| 5455778 | Ide et al. | Oct 1995 | A |
| 5465162 | Nishii et al. | Nov 1995 | A |
| 5469150 | Sitte | Nov 1995 | A |
| 5541914 | Krishnamoorthy et al. | Jul 1996 | A |
| 5543245 | Andrieu et al. | Aug 1996 | A |
| 5548584 | Beck et al. | Aug 1996 | A |
| 5548597 | Kayama et al. | Aug 1996 | A |
| 5566092 | Wang et al. | Oct 1996 | A |
| 5568356 | Schwartz | Oct 1996 | A |
| 5629870 | Farag et al. | May 1997 | A |
| 5650951 | Staver | Jul 1997 | A |
| 5663894 | Seth et al. | Sep 1997 | A |
| 5701394 | Arita et al. | Dec 1997 | A |
| 5710723 | Hoth et al. | Jan 1998 | A |
| 5715821 | Faupel | Feb 1998 | A |
| 5724475 | Kirsten | Mar 1998 | A |
| 5788789 | Cooper | Aug 1998 | A |
| 5794224 | Yufik | Aug 1998 | A |
| 5809490 | Guiver et al. | Sep 1998 | A |
| 5825646 | Keeler et al. | Oct 1998 | A |
| 5842034 | Bolstad et al. | Nov 1998 | A |
| 5852793 | Board et al. | Dec 1998 | A |
| 5854994 | Canada et al. | Dec 1998 | A |
| 5864773 | Barna et al. | Jan 1999 | A |
| 5874790 | Macks | Feb 1999 | A |
| 5884224 | McNabb et al. | Mar 1999 | A |
| 5895857 | Robinson et al. | Apr 1999 | A |
| 5917352 | Dunlap et al. | Jun 1999 | A |
| 5917428 | Discenzo et al. | Jun 1999 | A |
| 5924499 | Birchak et al. | Jul 1999 | A |
| 5941305 | Thrasher et al. | Aug 1999 | A |
| 5965819 | Piety et al. | Oct 1999 | A |
| 5991308 | Fuhrmann et al. | Nov 1999 | A |
| 6003030 | Kenner et al. | Dec 1999 | A |
| 6034662 | Kim | Mar 2000 | A |
| 6078847 | Eidson et al. | Jun 2000 | A |
| 6084911 | Ishikawa | Jul 2000 | A |
| 6111333 | Takahashi et al. | Aug 2000 | A |
| 6141355 | Palmer et al. | Oct 2000 | A |
| 6184713 | Agrawal et al. | Feb 2001 | B1 |
| 6198246 | Yutkowitz | Mar 2001 | B1 |
| 6222456 | Tice | Apr 2001 | B1 |
| 6272479 | Farry et al. | Aug 2001 | B1 |
| 6298308 | Reid et al. | Oct 2001 | B1 |
| 6298454 | Schleiss et al. | Oct 2001 | B1 |
| 6301572 | Harrison | Oct 2001 | B1 |
| 6330525 | Hays et al. | Dec 2001 | B1 |
| 6344747 | Lunghofer et al. | Feb 2002 | B1 |
| 6385513 | Murray et al. | May 2002 | B1 |
| 6388597 | Maezawa et al. | May 2002 | B1 |
| 6421341 | Han et al. | Jul 2002 | B1 |
| 6426602 | McCann et al. | Jul 2002 | B1 |
| 6434512 | Discenzo | Aug 2002 | B1 |
| 6446058 | Brown | Sep 2002 | B1 |
| 6448758 | Krahn et al. | Sep 2002 | B1 |
| 6484109 | Lofall | Nov 2002 | B1 |
| 6502042 | Eid et al. | Dec 2002 | B1 |
| 6502125 | Kenner et al. | Dec 2002 | B1 |
| 6554978 | Vandenborre | Apr 2003 | B1 |
| 6581048 | Werbos | Jun 2003 | B1 |
| 6628567 | Prosser et al. | Sep 2003 | B1 |
| 6633782 | Schleiss et al. | Oct 2003 | B1 |
| 6678268 | Francis et al. | Jan 2004 | B1 |
| 6694049 | Woodall | Feb 2004 | B1 |
| 6735579 | Woodall | May 2004 | B1 |
| 6737958 | Satyanarayana | May 2004 | B1 |
| 6789030 | Coyle et al. | Sep 2004 | B1 |
| 6795794 | Anastasio et al. | Sep 2004 | B2 |
| 6847353 | Sasselli et al. | Jan 2005 | B1 |
| 6853920 | Hsiung et al. | Feb 2005 | B2 |
| 6856600 | Russell et al. | Feb 2005 | B1 |
| 6865509 | Hsiung et al. | Mar 2005 | B1 |
| 6970758 | Shi et al. | Nov 2005 | B1 |
| 6977889 | Kawaguchi et al. | Dec 2005 | B1 |
| 6977890 | Oohashi et al. | Dec 2005 | B2 |
| 6982974 | Saleh et al. | Jan 2006 | B1 |
| 7027981 | Bizjak | Apr 2006 | B2 |
| 7043728 | Galpin | May 2006 | B1 |
| 7058712 | Vasko et al. | Jun 2006 | B1 |
| 7072295 | Benson et al. | Jul 2006 | B1 |
| 7135888 | Hutton et al. | Nov 2006 | B1 |
| 7142990 | Bouse et al. | Nov 2006 | B2 |
| 7174176 | Liu | Feb 2007 | B1 |
| 7206646 | Blevins et al. | Apr 2007 | B2 |
| 7218974 | Rumi et al. | May 2007 | B2 |
| 7225037 | Shani | May 2007 | B2 |
| 7228241 | Butas et al. | Jun 2007 | B1 |
| 7249284 | Shah et al. | Jul 2007 | B2 |
| 7304587 | Boaz | Dec 2007 | B2 |
| 7525360 | Wang et al. | Apr 2009 | B1 |
| 7539549 | Discenzo et al. | May 2009 | B1 |
| 7557702 | Eryurek et al. | Jul 2009 | B2 |
| 7581434 | Discenzo et al. | Sep 2009 | B1 |
| 7591183 | King | Sep 2009 | B2 |
| 7596803 | Barto et al. | Sep 2009 | B1 |
| 7657333 | Bradford et al. | Feb 2010 | B2 |
| 7710153 | Masleid et al. | May 2010 | B1 |
| 7836168 | Vasko et al. | Nov 2010 | B1 |
| 7896012 | Lee et al. | Mar 2011 | B1 |
| 8044793 | Eryurek et al. | Oct 2011 | B2 |
| 8057646 | Hinatsu et al. | Nov 2011 | B2 |
| 8060017 | Schlicht et al. | Nov 2011 | B2 |
| 8102188 | Chan et al. | Jan 2012 | B1 |
| 8200775 | Moore | Jun 2012 | B2 |
| 8229682 | El-Refaie et al. | Jul 2012 | B2 |
| 8352149 | Meacham | Jan 2013 | B2 |
| 8506656 | Turocy | Aug 2013 | B1 |
| 8571904 | Guru et al. | Oct 2013 | B2 |
| 8615374 | Discenzo | Dec 2013 | B1 |
| 8682930 | Das et al. | Mar 2014 | B2 |
| 8700360 | Garimella et al. | Apr 2014 | B2 |
| 8713476 | Martyn | Apr 2014 | B2 |
| 8761911 | Chapman et al. | Jun 2014 | B1 |
| 8766925 | Perlin et al. | Jul 2014 | B2 |
| 8799800 | Hood et al. | Aug 2014 | B2 |
| 8924033 | Goutard et al. | Dec 2014 | B2 |
| 8977578 | Cruz-Albrecht et al. | Mar 2015 | B1 |
| 9092593 | Nasle | Jul 2015 | B2 |
| 9104189 | Berges Gonzalez et al. | Aug 2015 | B2 |
| 9104271 | Adams et al. | Aug 2015 | B1 |
| 9225783 | Stephanson | Dec 2015 | B2 |
| 9314190 | Giuffrida et al. | Apr 2016 | B1 |
| 9403279 | Smith et al. | Aug 2016 | B2 |
| 9418339 | Leonard et al. | Aug 2016 | B1 |
| 9432298 | Smith | Aug 2016 | B1 |
| 9435684 | Baldwin | Sep 2016 | B2 |
| 9518459 | Bermudez et al. | Dec 2016 | B1 |
| 9557438 | Wessling et al. | Jan 2017 | B2 |
| 9567099 | Poux et al. | Feb 2017 | B2 |
| 9596298 | Bekiroglu et al. | Mar 2017 | B1 |
| 9604649 | Pastor et al. | Mar 2017 | B1 |
| 9617914 | Minto et al. | Apr 2017 | B2 |
| 9619999 | Stephanson | Apr 2017 | B2 |
| 9621173 | Xiu | Apr 2017 | B1 |
| 9645575 | Watson | May 2017 | B2 |
| 9696198 | Turner et al. | Jul 2017 | B2 |
| 9721210 | Brown | Aug 2017 | B1 |
| 9729639 | Sustaeta et al. | Aug 2017 | B2 |
| 9755984 | Feroz et al. | Sep 2017 | B1 |
| 9759213 | Bassett et al. | Sep 2017 | B2 |
| 9760174 | Letendre | Sep 2017 | B1 |
| 9800646 | Stamatakis et al. | Oct 2017 | B1 |
| 9804588 | Blevins et al. | Oct 2017 | B2 |
| 9824311 | Cruz-Albrecht et al. | Nov 2017 | B1 |
| 9843536 | Miller | Dec 2017 | B2 |
| 9846752 | Nasle | Dec 2017 | B2 |
| 9874923 | Brown et al. | Jan 2018 | B1 |
| 9912733 | T et al. | Mar 2018 | B2 |
| 9916702 | Rudenko et al. | Mar 2018 | B2 |
| 9976986 | Wayman et al. | May 2018 | B2 |
| 9979664 | Ho et al. | May 2018 | B2 |
| 9992088 | Ho et al. | Jun 2018 | B1 |
| 10045373 | Wang et al. | Aug 2018 | B2 |
| 10073447 | Uchida et al. | Sep 2018 | B2 |
| 10097403 | Anand et al. | Oct 2018 | B2 |
| 10120374 | Hosek et al. | Nov 2018 | B2 |
| 10168248 | Morey et al. | Jan 2019 | B1 |
| 10268191 | Lockwood et al. | Apr 2019 | B1 |
| 10338553 | Cella et al. | Jul 2019 | B2 |
| 10378994 | Raman et al. | Aug 2019 | B2 |
| 10382556 | Ferre et al. | Aug 2019 | B2 |
| 10394210 | Cella et al. | Aug 2019 | B2 |
| 10401846 | Chen et al. | Sep 2019 | B2 |
| 10409926 | Mehta | Sep 2019 | B2 |
| 10476985 | Spina et al. | Nov 2019 | B1 |
| 10545472 | Cella et al. | Jan 2020 | B2 |
| 10545473 | Cella et al. | Jan 2020 | B2 |
| 10545474 | Cella et al. | Jan 2020 | B2 |
| 10560388 | Ho et al. | Feb 2020 | B2 |
| 10564638 | Lockwood et al. | Feb 2020 | B1 |
| 10678225 | Kidd et al. | Jun 2020 | B2 |
| 10706693 | Castillo Canales et al. | Jul 2020 | B1 |
| 10732582 | Weast et al. | Aug 2020 | B2 |
| 10739746 | Aparicio Ojea et al. | Aug 2020 | B2 |
| 10807804 | Kulick et al. | Oct 2020 | B2 |
| 10824140 | Cella et al. | Nov 2020 | B2 |
| 10831093 | Lablans | Nov 2020 | B1 |
| 10983507 | Cella et al. | Apr 2021 | B2 |
| 11029680 | Cella et al. | Jun 2021 | B2 |
| 20010015918 | Bhatnagar | Aug 2001 | A1 |
| 20010035912 | Cooper et al. | Nov 2001 | A1 |
| 20020002414 | Hisiung et al. | Jan 2002 | A1 |
| 20020004694 | McLeod et al. | Jan 2002 | A1 |
| 20020013664 | Strackeljan et al. | Jan 2002 | A1 |
| 20020018545 | Crichlow | Feb 2002 | A1 |
| 20020032544 | Reid et al. | Mar 2002 | A1 |
| 20020075883 | Dell et al. | Jun 2002 | A1 |
| 20020077711 | Belvins et al. | Jun 2002 | A1 |
| 20020084815 | Murphy et al. | Jul 2002 | A1 |
| 20020109568 | Wohlfarth | Aug 2002 | A1 |
| 20020129661 | Clarke et al. | Sep 2002 | A1 |
| 20020152037 | Sunshine et al. | Oct 2002 | A1 |
| 20020174708 | Mattes | Nov 2002 | A1 |
| 20020177878 | Poore et al. | Nov 2002 | A1 |
| 20020178277 | Laksono | Nov 2002 | A1 |
| 20020181799 | Matsugu et al. | Dec 2002 | A1 |
| 20030028268 | Eryurek et al. | Feb 2003 | A1 |
| 20030054960 | Bedard | Mar 2003 | A1 |
| 20030061004 | Discenzo | Mar 2003 | A1 |
| 20030069648 | Douglas et al. | Apr 2003 | A1 |
| 20030070059 | Dally et al. | Apr 2003 | A1 |
| 20030083756 | Hsiung et al. | May 2003 | A1 |
| 20030088529 | Klinker et al. | May 2003 | A1 |
| 20030094992 | Geysen | May 2003 | A1 |
| 20030101575 | Green et al. | Jun 2003 | A1 |
| 20030118081 | Philips et al. | Jun 2003 | A1 |
| 20030137648 | Van et al. | Jul 2003 | A1 |
| 20030147351 | Greenlee | Aug 2003 | A1 |
| 20030149456 | Rottenberg et al. | Aug 2003 | A1 |
| 20030151397 | Murphy et al. | Aug 2003 | A1 |
| 20030158795 | Markham et al. | Aug 2003 | A1 |
| 20030165398 | Waldo et al. | Sep 2003 | A1 |
| 20030174681 | Gilberton et al. | Sep 2003 | A1 |
| 20030200022 | Streichsbier et al. | Oct 2003 | A1 |
| 20030229471 | Guralnik et al. | Dec 2003 | A1 |
| 20040019461 | Bouse et al. | Jan 2004 | A1 |
| 20040024568 | Eryurek et al. | Feb 2004 | A1 |
| 20040044744 | Grosner et al. | Mar 2004 | A1 |
| 20040068416 | Solomon | Apr 2004 | A1 |
| 20040093516 | Hornbeek et al. | May 2004 | A1 |
| 20040102924 | Jarrell et al. | May 2004 | A1 |
| 20040109065 | Tokunaga | Jun 2004 | A1 |
| 20040120359 | Frenzel et al. | Jun 2004 | A1 |
| 20040138832 | Judd | Jul 2004 | A1 |
| 20040165783 | Reynolds et al. | Aug 2004 | A1 |
| 20040172147 | Dillon | Sep 2004 | A1 |
| 20040186927 | Eryurek et al. | Sep 2004 | A1 |
| 20040194557 | Nagase | Oct 2004 | A1 |
| 20040205097 | Toumazou et al. | Oct 2004 | A1 |
| 20040259563 | Morton et al. | Dec 2004 | A1 |
| 20040267395 | Discenzo et al. | Dec 2004 | A1 |
| 20050007249 | Eryurek et al. | Jan 2005 | A1 |
| 20050010462 | Dausch et al. | Jan 2005 | A1 |
| 20050010958 | Rakib et al. | Jan 2005 | A1 |
| 20050011266 | Robinson et al. | Jan 2005 | A1 |
| 20050011278 | Brown et al. | Jan 2005 | A1 |
| 20050090756 | Wolf et al. | Apr 2005 | A1 |
| 20050100172 | Schliep et al. | May 2005 | A1 |
| 20050132808 | Brown et al. | Jun 2005 | A1 |
| 20050162258 | King | Jul 2005 | A1 |
| 20050165581 | Roba et al. | Jul 2005 | A1 |
| 20050200497 | Smithson et al. | Sep 2005 | A1 |
| 20050204820 | Treiber et al. | Sep 2005 | A1 |
| 20050240289 | Hoyte et al. | Oct 2005 | A1 |
| 20050246140 | Oconnor et al. | Nov 2005 | A1 |
| 20060006997 | Rose-Pehrsson et al. | Jan 2006 | A1 |
| 20060010230 | Karklins et al. | Jan 2006 | A1 |
| 20060020202 | Mathew et al. | Jan 2006 | A1 |
| 20060026164 | Jung et al. | Feb 2006 | A1 |
| 20060028993 | Yang et al. | Feb 2006 | A1 |
| 20060034569 | Shih et al. | Feb 2006 | A1 |
| 20060056372 | Karaoguz et al. | Mar 2006 | A1 |
| 20060069689 | Karklins et al. | Mar 2006 | A1 |
| 20060073013 | Emigholz et al. | Apr 2006 | A1 |
| 20060129340 | Straznicky | Jun 2006 | A1 |
| 20060150738 | Leigh | Jul 2006 | A1 |
| 20060152636 | Matsukawa et al. | Jul 2006 | A1 |
| 20060155900 | Sagues et al. | Jul 2006 | A1 |
| 20060167638 | Murphy et al. | Jul 2006 | A1 |
| 20060178762 | Wroblewski et al. | Aug 2006 | A1 |
| 20060223634 | Feldman et al. | Oct 2006 | A1 |
| 20060224254 | Rumi et al. | Oct 2006 | A1 |
| 20060224545 | Keith | Oct 2006 | A1 |
| 20060229739 | Morikawa | Oct 2006 | A1 |
| 20060241907 | Armstrong et al. | Oct 2006 | A1 |
| 20060250959 | Porat | Nov 2006 | A1 |
| 20060259163 | Hsiung et al. | Nov 2006 | A1 |
| 20060271617 | Hughes et al. | Nov 2006 | A1 |
| 20060271677 | Mercier | Nov 2006 | A1 |
| 20060272859 | Pastusek et al. | Dec 2006 | A1 |
| 20060274153 | Levien et al. | Dec 2006 | A1 |
| 20060279279 | Foley | Dec 2006 | A1 |
| 20070025382 | Jones et al. | Feb 2007 | A1 |
| 20070034019 | Doihara et al. | Feb 2007 | A1 |
| 20070047444 | Leroy et al. | Mar 2007 | A1 |
| 20070056379 | Nassar et al. | Mar 2007 | A1 |
| 20070067678 | Hosek et al. | Mar 2007 | A1 |
| 20070076590 | Galpin et al. | Apr 2007 | A1 |
| 20070078802 | Bestgen et al. | Apr 2007 | A1 |
| 20070111661 | Bargroff et al. | May 2007 | A1 |
| 20070118286 | Wang et al. | May 2007 | A1 |
| 20070135984 | Breed et al. | Jun 2007 | A1 |
| 20070204023 | Ohta | Aug 2007 | A1 |
| 20070207752 | Behzad | Sep 2007 | A1 |
| 20070208483 | Rabin | Sep 2007 | A1 |
| 20070260656 | Wiig | Nov 2007 | A1 |
| 20070270671 | Gal | Nov 2007 | A1 |
| 20070277613 | Iwatsubo et al. | Dec 2007 | A1 |
| 20070280332 | Srikanteswara et al. | Dec 2007 | A1 |
| 20070296368 | Woodland et al. | Dec 2007 | A1 |
| 20080049747 | McNaughton et al. | Feb 2008 | A1 |
| 20080065331 | Pastusek et al. | Mar 2008 | A1 |
| 20080079029 | Williams | Apr 2008 | A1 |
| 20080101683 | Zombo et al. | May 2008 | A1 |
| 20080112140 | Wong | May 2008 | A1 |
| 20080141072 | Kalgren et al. | Jun 2008 | A1 |
| 20080156094 | Holmes et al. | Jul 2008 | A1 |
| 20080162302 | Sundaresan et al. | Jul 2008 | A1 |
| 20080169914 | Albertson et al. | Jul 2008 | A1 |
| 20080170853 | Rakib et al. | Jul 2008 | A1 |
| 20080194975 | MacQuarrie et al. | Aug 2008 | A1 |
| 20080209046 | Karkanias et al. | Aug 2008 | A1 |
| 20080224845 | Bires | Sep 2008 | A1 |
| 20080234964 | Miyasaka et al. | Sep 2008 | A1 |
| 20080243342 | Breed | Oct 2008 | A1 |
| 20080252481 | Vacar et al. | Oct 2008 | A1 |
| 20080262759 | Bosl et al. | Oct 2008 | A1 |
| 20080278197 | Murotake | Nov 2008 | A1 |
| 20080288321 | Dillon et al. | Nov 2008 | A1 |
| 20080319279 | Ramsay et al. | Dec 2008 | A1 |
| 20080320182 | Rugo et al. | Dec 2008 | A1 |
| 20090003599 | Hart et al. | Jan 2009 | A1 |
| 20090031419 | Laksono | Jan 2009 | A1 |
| 20090055126 | Yanovich et al. | Feb 2009 | A1 |
| 20090061775 | Warren et al. | Mar 2009 | A1 |
| 20090063026 | Laubender | Mar 2009 | A1 |
| 20090063739 | Weddle | Mar 2009 | A1 |
| 20090064250 | Nakata | Mar 2009 | A1 |
| 20090066505 | Jensen et al. | Mar 2009 | A1 |
| 20090071264 | Wray | Mar 2009 | A1 |
| 20090083019 | Nasle | Mar 2009 | A1 |
| 20090084657 | Brandt et al. | Apr 2009 | A1 |
| 20090089682 | Baier et al. | Apr 2009 | A1 |
| 20090093975 | Judd | Apr 2009 | A1 |
| 20090135761 | Khandekar et al. | May 2009 | A1 |
| 20090147673 | Niigata | Jun 2009 | A1 |
| 20090171950 | Lunenfeld | Jul 2009 | A1 |
| 20090194274 | Del et al. | Aug 2009 | A1 |
| 20090204232 | Guru et al. | Aug 2009 | A1 |
| 20090204234 | Sustaeta et al. | Aug 2009 | A1 |
| 20090204237 | Sustaeta et al. | Aug 2009 | A1 |
| 20090204245 | Sustaeta et al. | Aug 2009 | A1 |
| 20090204267 | Sustaeta et al. | Aug 2009 | A1 |
| 20090210081 | Sustaeta et al. | Aug 2009 | A1 |
| 20090222541 | Monga et al. | Sep 2009 | A1 |
| 20090222921 | Mukhopadhyay et al. | Sep 2009 | A1 |
| 20090228224 | Spanier et al. | Sep 2009 | A1 |
| 20090231153 | Hauenstein et al. | Sep 2009 | A1 |
| 20090243732 | Tarng et al. | Oct 2009 | A1 |
| 20090256734 | Wu | Oct 2009 | A1 |
| 20090256817 | Perlin et al. | Oct 2009 | A1 |
| 20090303197 | Bonczek et al. | Dec 2009 | A1 |
| 20100027426 | Nair et al. | Feb 2010 | A1 |
| 20100030521 | Akhrarov et al. | Feb 2010 | A1 |
| 20100060296 | Jiang et al. | Mar 2010 | A1 |
| 20100064026 | Brown et al. | Mar 2010 | A1 |
| 20100082126 | Matsushita | Apr 2010 | A1 |
| 20100094981 | Cordray et al. | Apr 2010 | A1 |
| 20100101860 | Wassermann et al. | Apr 2010 | A1 |
| 20100114514 | Wang et al. | May 2010 | A1 |
| 20100114806 | Harrison et al. | May 2010 | A1 |
| 20100138026 | Sugishima et al. | Jun 2010 | A1 |
| 20100148940 | Gelvin et al. | Jun 2010 | A1 |
| 20100149007 | Zushi et al. | Jun 2010 | A1 |
| 20100156632 | Hyland et al. | Jun 2010 | A1 |
| 20100169030 | Parlos | Jul 2010 | A1 |
| 20100212422 | Allen et al. | Aug 2010 | A1 |
| 20100216523 | Sebastiano et al. | Aug 2010 | A1 |
| 20100241601 | Carson et al. | Sep 2010 | A1 |
| 20100241891 | Beasley | Sep 2010 | A1 |
| 20100245105 | Smith | Sep 2010 | A1 |
| 20100249976 | Aharoni et al. | Sep 2010 | A1 |
| 20100256795 | McLaughlin et al. | Oct 2010 | A1 |
| 20100262398 | Baek et al. | Oct 2010 | A1 |
| 20100262401 | Sterzing et al. | Oct 2010 | A1 |
| 20100268470 | Kamal et al. | Oct 2010 | A1 |
| 20100271199 | Belov et al. | Oct 2010 | A1 |
| 20100278086 | Pochiraju et al. | Nov 2010 | A1 |
| 20100280343 | Huiku | Nov 2010 | A1 |
| 20100313311 | Spizig et al. | Dec 2010 | A1 |
| 20100316232 | Acero et al. | Dec 2010 | A1 |
| 20100318641 | Bullard et al. | Dec 2010 | A1 |
| 20110019693 | Fu et al. | Jan 2011 | A1 |
| 20110055087 | Chen-Ritzo et al. | Mar 2011 | A1 |
| 20110061015 | Drees et al. | Mar 2011 | A1 |
| 20110071794 | Bronczyk et al. | Mar 2011 | A1 |
| 20110071963 | Piovesan et al. | Mar 2011 | A1 |
| 20110078089 | Hamm et al. | Mar 2011 | A1 |
| 20110078222 | Wegener | Mar 2011 | A1 |
| 20110092164 | Spanhake | Apr 2011 | A1 |
| 20110125921 | Karenos et al. | May 2011 | A1 |
| 20110126047 | Anderson et al. | May 2011 | A1 |
| 20110157077 | Martin et al. | Jun 2011 | A1 |
| 20110178737 | Hudson et al. | Jul 2011 | A1 |
| 20110181437 | Gershinsky et al. | Jul 2011 | A1 |
| 20110184547 | Loutfi | Jul 2011 | A1 |
| 20110185366 | Klingenberg et al. | Jul 2011 | A1 |
| 20110199072 | Kerkman et al. | Aug 2011 | A1 |
| 20110208361 | Hildebrand et al. | Aug 2011 | A1 |
| 20110282508 | Goutard et al. | Nov 2011 | A1 |
| 20110288796 | Peczalski et al. | Nov 2011 | A1 |
| 20120013497 | Katsuki et al. | Jan 2012 | A1 |
| 20120025526 | Luo et al. | Feb 2012 | A1 |
| 20120028577 | Rodriguez et al. | Feb 2012 | A1 |
| 20120065901 | Bechhoefer et al. | Mar 2012 | A1 |
| 20120072136 | Hedin | Mar 2012 | A1 |
| 20120095574 | Greenlee | Apr 2012 | A1 |
| 20120101912 | Sen et al. | Apr 2012 | A1 |
| 20120109851 | Sanders | May 2012 | A1 |
| 20120111978 | Murphy et al. | May 2012 | A1 |
| 20120130659 | Chaves | May 2012 | A1 |
| 20120166363 | He et al. | Jun 2012 | A1 |
| 20120197596 | Comi | Aug 2012 | A1 |
| 20120219089 | Murakami et al. | Aug 2012 | A1 |
| 20120232847 | Horton et al. | Sep 2012 | A1 |
| 20120239317 | Lin | Sep 2012 | A1 |
| 20120245436 | Rutkove et al. | Sep 2012 | A1 |
| 20120246055 | Schlifstein et al. | Sep 2012 | A1 |
| 20120254803 | Grist et al. | Oct 2012 | A1 |
| 20120265359 | Das et al. | Oct 2012 | A1 |
| 20120284291 | Brown et al. | Nov 2012 | A1 |
| 20120296899 | Adams | Nov 2012 | A1 |
| 20120303625 | Ciodaru et al. | Nov 2012 | A1 |
| 20120323741 | Rangachari et al. | Dec 2012 | A1 |
| 20120330495 | Geib et al. | Dec 2012 | A1 |
| 20130003238 | Bush et al. | Jan 2013 | A1 |
| 20130027015 | Park | Jan 2013 | A1 |
| 20130027561 | Lee et al. | Jan 2013 | A1 |
| 20130054175 | Saloio et al. | Feb 2013 | A1 |
| 20130060524 | Liao | Mar 2013 | A1 |
| 20130115535 | Delfino et al. | May 2013 | A1 |
| 20130117438 | Gupta et al. | May 2013 | A1 |
| 20130124719 | Espinosa | May 2013 | A1 |
| 20130163619 | Stephanson | Jun 2013 | A1 |
| 20130164092 | Kondo | Jun 2013 | A1 |
| 20130179124 | Patel et al. | Jul 2013 | A1 |
| 20130184927 | Daniel et al. | Jul 2013 | A1 |
| 20130184928 | Kerkhof et al. | Jul 2013 | A1 |
| 20130211555 | Lawson et al. | Aug 2013 | A1 |
| 20130211559 | Lawson et al. | Aug 2013 | A1 |
| 20130212613 | Velasco et al. | Aug 2013 | A1 |
| 20130217598 | Ludwig et al. | Aug 2013 | A1 |
| 20130218451 | Yamada | Aug 2013 | A1 |
| 20130218493 | Weddle et al. | Aug 2013 | A1 |
| 20130218521 | Weddle et al. | Aug 2013 | A1 |
| 20130227181 | Martin et al. | Aug 2013 | A1 |
| 20130230196 | Negishi et al. | Sep 2013 | A1 |
| 20130243963 | Rina et al. | Sep 2013 | A1 |
| 20130245795 | McGreevy et al. | Sep 2013 | A1 |
| 20130282149 | Kuntagod et al. | Oct 2013 | A1 |
| 20130297377 | Easo et al. | Nov 2013 | A1 |
| 20130311832 | Lad et al. | Nov 2013 | A1 |
| 20130313827 | Lovmand et al. | Nov 2013 | A1 |
| 20130326053 | Bauer et al. | Dec 2013 | A1 |
| 20140012791 | Grichnik et al. | Jan 2014 | A1 |
| 20140018999 | Adams et al. | Jan 2014 | A1 |
| 20140024516 | Kusumoto et al. | Jan 2014 | A1 |
| 20140032605 | Aydin et al. | Jan 2014 | A1 |
| 20140046614 | Pettersson | Feb 2014 | A1 |
| 20140047064 | Maturana et al. | Feb 2014 | A1 |
| 20140067289 | Baldwin | Mar 2014 | A1 |
| 20140074433 | Crepet | Mar 2014 | A1 |
| 20140079248 | Short et al. | Mar 2014 | A1 |
| 20140097691 | Jackson et al. | Apr 2014 | A1 |
| 20140100738 | Itatsu et al. | Apr 2014 | A1 |
| 20140100912 | Bursey | Apr 2014 | A1 |
| 20140120972 | Hartman | May 2014 | A1 |
| 20140143579 | Grokop et al. | May 2014 | A1 |
| 20140155751 | Banjanin et al. | Jun 2014 | A1 |
| 20140161135 | Acharya et al. | Jun 2014 | A1 |
| 20140167810 | Neti et al. | Jun 2014 | A1 |
| 20140176203 | Matheny et al. | Jun 2014 | A1 |
| 20140188434 | Steinbrecher et al. | Jul 2014 | A1 |
| 20140198615 | Ray | Jul 2014 | A1 |
| 20140201571 | Hosek et al. | Jul 2014 | A1 |
| 20140210473 | Campbell et al. | Jul 2014 | A1 |
| 20140222971 | Cooper et al. | Aug 2014 | A1 |
| 20140232534 | Birnbaum et al. | Aug 2014 | A1 |
| 20140251688 | Turner et al. | Sep 2014 | A1 |
| 20140251836 | Feeney | Sep 2014 | A1 |
| 20140262392 | Petrossians et al. | Sep 2014 | A1 |
| 20140271449 | McAlister et al. | Sep 2014 | A1 |
| 20140278312 | Blevins et al. | Sep 2014 | A1 |
| 20140279574 | Gettings et al. | Sep 2014 | A1 |
| 20140280678 | Blevins et al. | Sep 2014 | A1 |
| 20140282257 | Nixon et al. | Sep 2014 | A1 |
| 20140288876 | Donaldson | Sep 2014 | A1 |
| 20140304201 | Hyldgaard et al. | Oct 2014 | A1 |
| 20140309821 | Poux et al. | Oct 2014 | A1 |
| 20140313303 | Davis et al. | Oct 2014 | A1 |
| 20140314099 | Dress | Oct 2014 | A1 |
| 20140324367 | Garvey et al. | Oct 2014 | A1 |
| 20140324389 | Baldwin et al. | Oct 2014 | A1 |
| 20140336791 | Asenjo et al. | Nov 2014 | A1 |
| 20140336878 | Yanai et al. | Nov 2014 | A1 |
| 20140337277 | Asenjo et al. | Nov 2014 | A1 |
| 20140352444 | Nicq | Dec 2014 | A1 |
| 20140354284 | Pai et al. | Dec 2014 | A1 |
| 20140376405 | Erickson et al. | Dec 2014 | A1 |
| 20140378810 | Davis et al. | Dec 2014 | A1 |
| 20140379102 | Tian et al. | Dec 2014 | A1 |
| 20150016185 | King Liu et al. | Jan 2015 | A1 |
| 20150020088 | Velasco et al. | Jan 2015 | A1 |
| 20150029864 | Raileanu et al. | Jan 2015 | A1 |
| 20150040051 | Fuhrmann et al. | Feb 2015 | A1 |
| 20150046127 | Chen et al. | Feb 2015 | A1 |
| 20150046697 | Galpin et al. | Feb 2015 | A1 |
| 20150055633 | Wu et al. | Feb 2015 | A1 |
| 20150059442 | Liljenberg et al. | Mar 2015 | A1 |
| 20150067119 | Estevez et al. | Mar 2015 | A1 |
| 20150070145 | Mar et al. | Mar 2015 | A1 |
| 20150080044 | McHenry et al. | Mar 2015 | A1 |
| 20150097707 | Nelson et al. | Apr 2015 | A1 |
| 20150112488 | Hoehn et al. | Apr 2015 | A1 |
| 20150120230 | Banhegyesi | Apr 2015 | A1 |
| 20150121468 | Park et al. | Apr 2015 | A1 |
| 20150134954 | Walley et al. | May 2015 | A1 |
| 20150142384 | Chao et al. | May 2015 | A1 |
| 20150151960 | McCleland et al. | Jun 2015 | A1 |
| 20150153757 | Meagher | Jun 2015 | A1 |
| 20150154136 | Markovic et al. | Jun 2015 | A1 |
| 20150180760 | Rickard | Jun 2015 | A1 |
| 20150180986 | Bisdikian et al. | Jun 2015 | A1 |
| 20150185716 | Wichmann et al. | Jul 2015 | A1 |
| 20150186483 | Tappan et al. | Jul 2015 | A1 |
| 20150192439 | Mihelich et al. | Jul 2015 | A1 |
| 20150223731 | Sahin | Aug 2015 | A1 |
| 20150229530 | Silvers | Aug 2015 | A1 |
| 20150233731 | Torpy et al. | Aug 2015 | A1 |
| 20150237563 | Maric et al. | Aug 2015 | A1 |
| 20150248375 | Houel et al. | Sep 2015 | A1 |
| 20150249806 | Gabel | Sep 2015 | A1 |
| 20150271106 | Walker et al. | Sep 2015 | A1 |
| 20150277399 | Maturana et al. | Oct 2015 | A1 |
| 20150277406 | Maturana et al. | Oct 2015 | A1 |
| 20150278839 | Hansen | Oct 2015 | A1 |
| 20150288257 | Cooper et al. | Oct 2015 | A1 |
| 20150302664 | Miller | Oct 2015 | A1 |
| 20150317197 | Blair | Nov 2015 | A1 |
| 20150323510 | Huynh et al. | Nov 2015 | A1 |
| 20150323936 | Junk | Nov 2015 | A1 |
| 20150330640 | Stork Genannt Wersborg | Nov 2015 | A1 |
| 20150330950 | Bechhoefer | Nov 2015 | A1 |
| 20150331805 | Daga et al. | Nov 2015 | A1 |
| 20150331928 | Ghaemi | Nov 2015 | A1 |
| 20150351084 | Werb | Dec 2015 | A1 |
| 20150354607 | Avni | Dec 2015 | A1 |
| 20150355245 | Ordanis | Dec 2015 | A1 |
| 20150357948 | Goldstein | Dec 2015 | A1 |
| 20150379510 | Smith | Dec 2015 | A1 |
| 20160007102 | Raza et al. | Jan 2016 | A1 |
| 20160011692 | Heim et al. | Jan 2016 | A1 |
| 20160026172 | Steele et al. | Jan 2016 | A1 |
| 20160026173 | Willis et al. | Jan 2016 | A1 |
| 20160026729 | Gil et al. | Jan 2016 | A1 |
| 20160028605 | Gil et al. | Jan 2016 | A1 |
| 20160047204 | Donderici et al. | Feb 2016 | A1 |
| 20160048110 | Hilemon | Feb 2016 | A1 |
| 20160048399 | Shaw | Feb 2016 | A1 |
| 20160054284 | Washburn | Feb 2016 | A1 |
| 20160054951 | Mathur et al. | Feb 2016 | A1 |
| 20160078695 | McClintic et al. | Mar 2016 | A1 |
| 20160091398 | Pluemer | Mar 2016 | A1 |
| 20160097674 | Zusman | Apr 2016 | A1 |
| 20160098647 | Nixon et al. | Apr 2016 | A1 |
| 20160104330 | Rudenko et al. | Apr 2016 | A1 |
| 20160130928 | Torrione | May 2016 | A1 |
| 20160135109 | Hampel et al. | May 2016 | A1 |
| 20160138492 | Levy et al. | May 2016 | A1 |
| 20160142160 | Walker et al. | May 2016 | A1 |
| 20160142868 | Kulkarni et al. | May 2016 | A1 |
| 20160143541 | He et al. | May 2016 | A1 |
| 20160147204 | Wichmann et al. | May 2016 | A1 |
| 20160153806 | Ciasulli et al. | Jun 2016 | A1 |
| 20160161028 | Arai et al. | Jun 2016 | A1 |
| 20160163186 | Davidson et al. | Jun 2016 | A1 |
| 20160171846 | Brav et al. | Jun 2016 | A1 |
| 20160182309 | Maturana et al. | Jun 2016 | A1 |
| 20160187864 | Choe et al. | Jun 2016 | A1 |
| 20160196124 | Vedula et al. | Jul 2016 | A1 |
| 20160196375 | Nasle | Jul 2016 | A1 |
| 20160196758 | Causevic et al. | Jul 2016 | A1 |
| 20160209831 | Pal | Jul 2016 | A1 |
| 20160210834 | Dayal | Jul 2016 | A1 |
| 20160215614 | Song et al. | Jul 2016 | A1 |
| 20160217384 | Leonard et al. | Jul 2016 | A1 |
| 20160219024 | Verzun et al. | Jul 2016 | A1 |
| 20160219348 | Formo et al. | Jul 2016 | A1 |
| 20160220198 | Proud | Aug 2016 | A1 |
| 20160245027 | Gumus et al. | Aug 2016 | A1 |
| 20160245686 | Pal et al. | Aug 2016 | A1 |
| 20160255420 | McCleland et al. | Sep 2016 | A1 |
| 20160256063 | Friedman et al. | Sep 2016 | A1 |
| 20160258836 | Raman et al. | Sep 2016 | A1 |
| 20160262687 | Vaidyanathan et al. | Sep 2016 | A1 |
| 20160273354 | Chen et al. | Sep 2016 | A1 |
| 20160275376 | Kant | Sep 2016 | A1 |
| 20160275414 | Towal | Sep 2016 | A1 |
| 20160282820 | Perez et al. | Sep 2016 | A1 |
| 20160282872 | Ahmed et al. | Sep 2016 | A1 |
| 20160285840 | Smith et al. | Sep 2016 | A1 |
| 20160301991 | Loychik et al. | Oct 2016 | A1 |
| 20160302019 | Smith et al. | Oct 2016 | A1 |
| 20160305236 | Forsberg et al. | Oct 2016 | A1 |
| 20160310062 | Larson | Oct 2016 | A1 |
| 20160328979 | Postrel | Nov 2016 | A1 |
| 20160330137 | Avci | Nov 2016 | A1 |
| 20160334306 | Chiu et al. | Nov 2016 | A1 |
| 20160337127 | Schultz et al. | Nov 2016 | A1 |
| 20160350671 | Morris et al. | Dec 2016 | A1 |
| 20160356125 | Bello et al. | Dec 2016 | A1 |
| 20160359680 | Parandehgheibi et al. | Dec 2016 | A1 |
| 20160378076 | Hill et al. | Dec 2016 | A1 |
| 20160378086 | Plymill et al. | Dec 2016 | A1 |
| 20160379282 | Hill et al. | Dec 2016 | A1 |
| 20170003677 | Hsu et al. | Jan 2017 | A1 |
| 20170004697 | Boerhout | Jan 2017 | A1 |
| 20170006135 | Siebel et al. | Jan 2017 | A1 |
| 20170012861 | Blumenthal et al. | Jan 2017 | A1 |
| 20170012868 | Ho et al. | Jan 2017 | A1 |
| 20170012884 | Ho et al. | Jan 2017 | A1 |
| 20170012885 | Ho | Jan 2017 | A1 |
| 20170012905 | Ho et al. | Jan 2017 | A1 |
| 20170030349 | Bassett et al. | Feb 2017 | A1 |
| 20170031348 | Willis et al. | Feb 2017 | A1 |
| 20170032281 | Hsu | Feb 2017 | A1 |
| 20170037691 | Savage et al. | Feb 2017 | A1 |
| 20170037721 | Lovorn et al. | Feb 2017 | A1 |
| 20170046458 | Meagher et al. | Feb 2017 | A1 |
| 20170053461 | Pal et al. | Feb 2017 | A1 |
| 20170068782 | Pillai et al. | Mar 2017 | A1 |
| 20170070842 | Kulp et al. | Mar 2017 | A1 |
| 20170074715 | Bartos et al. | Mar 2017 | A1 |
| 20170075320 | Day et al. | Mar 2017 | A1 |
| 20170075552 | Berenbaum et al. | Mar 2017 | A1 |
| 20170082101 | Urdaneta et al. | Mar 2017 | A1 |
| 20170096889 | Blanckaert et al. | Apr 2017 | A1 |
| 20170097615 | Mazur et al. | Apr 2017 | A1 |
| 20170097617 | Tegnell et al. | Apr 2017 | A1 |
| 20170102678 | Nixon et al. | Apr 2017 | A1 |
| 20170102693 | Kidd et al. | Apr 2017 | A1 |
| 20170104736 | Seul et al. | Apr 2017 | A1 |
| 20170114626 | Bardapurkar et al. | Apr 2017 | A1 |
| 20170124487 | Szeto et al. | May 2017 | A1 |
| 20170130700 | Sakaguchi et al. | May 2017 | A1 |
| 20170132910 | Chen et al. | May 2017 | A1 |
| 20170147674 | Procops et al. | May 2017 | A1 |
| 20170149605 | Strasser | May 2017 | A1 |
| 20170152729 | Gleitman et al. | Jun 2017 | A1 |
| 20170163436 | McLaughlin et al. | Jun 2017 | A1 |
| 20170170924 | Soro et al. | Jun 2017 | A1 |
| 20170173458 | Billington et al. | Jun 2017 | A1 |
| 20170175645 | Devarakonda et al. | Jun 2017 | A1 |
| 20170176033 | Tan et al. | Jun 2017 | A1 |
| 20170180221 | Appel et al. | Jun 2017 | A1 |
| 20170200092 | Kisilev | Jul 2017 | A1 |
| 20170205451 | Moinuddin | Jul 2017 | A1 |
| 20170206464 | Clayton et al. | Jul 2017 | A1 |
| 20170207926 | Gil et al. | Jul 2017 | A1 |
| 20170215261 | Potucek et al. | Jul 2017 | A1 |
| 20170222999 | Banga et al. | Aug 2017 | A1 |
| 20170223046 | Singh | Aug 2017 | A1 |
| 20170238072 | Mackie et al. | Aug 2017 | A1 |
| 20170239594 | Bowers et al. | Aug 2017 | A1 |
| 20170244749 | Shulman et al. | Aug 2017 | A1 |
| 20170249282 | Novet | Aug 2017 | A1 |
| 20170257653 | Farré Guiu et al. | Sep 2017 | A1 |
| 20170284186 | Samuel et al. | Oct 2017 | A1 |
| 20170284902 | Nolan et al. | Oct 2017 | A1 |
| 20170300753 | Billi et al. | Oct 2017 | A1 |
| 20170307466 | Brennan et al. | Oct 2017 | A1 |
| 20170310338 | Hori | Oct 2017 | A1 |
| 20170310747 | Cohn et al. | Oct 2017 | A1 |
| 20170312614 | Tran et al. | Nov 2017 | A1 |
| 20170329307 | Castillo-Effen et al. | Nov 2017 | A1 |
| 20170331577 | Parkvall et al. | Nov 2017 | A1 |
| 20170331670 | Parkvall et al. | Nov 2017 | A1 |
| 20170332049 | Zhang | Nov 2017 | A1 |
| 20170336447 | Valenti et al. | Nov 2017 | A1 |
| 20170338835 | Priller et al. | Nov 2017 | A1 |
| 20170339022 | Hegde et al. | Nov 2017 | A1 |
| 20170352010 | Son et al. | Dec 2017 | A1 |
| 20170353537 | Crabtree et al. | Dec 2017 | A1 |
| 20170371311 | Aparicio Ojea et al. | Dec 2017 | A1 |
| 20170372534 | Steketee et al. | Dec 2017 | A1 |
| 20180007055 | Infante-Lopez et al. | Jan 2018 | A1 |
| 20180007131 | Cohn et al. | Jan 2018 | A1 |
| 20180023986 | Kruusmaa et al. | Jan 2018 | A1 |
| 20180034694 | Farnham | Feb 2018 | A1 |
| 20180035134 | Pang et al. | Feb 2018 | A1 |
| 20180035195 | Bjelosevic et al. | Feb 2018 | A1 |
| 20180052428 | Abramov | Feb 2018 | A1 |
| 20180054490 | Wadhwa et al. | Feb 2018 | A1 |
| 20180059685 | Baroudi et al. | Mar 2018 | A1 |
| 20180062553 | Van et al. | Mar 2018 | A1 |
| 20180066658 | Philippe | Mar 2018 | A1 |
| 20180082501 | Kochhar et al. | Mar 2018 | A1 |
| 20180091516 | Nixon et al. | Mar 2018 | A1 |
| 20180095455 | Silva et al. | Apr 2018 | A1 |
| 20180096243 | Patil et al. | Apr 2018 | A1 |
| 20180124547 | Patil | May 2018 | A1 |
| 20180135401 | Dykstra et al. | May 2018 | A1 |
| 20180142905 | Plourde et al. | May 2018 | A1 |
| 20180183874 | Cook | Jun 2018 | A1 |
| 20180188704 | Cella et al. | Jul 2018 | A1 |
| 20180188714 | Cella et al. | Jul 2018 | A1 |
| 20180189684 | Vasvani | Jul 2018 | A1 |
| 20180191867 | Siebel et al. | Jul 2018 | A1 |
| 20180203442 | Kotlyarov | Jul 2018 | A1 |
| 20180210425 | Cella et al. | Jul 2018 | A1 |
| 20180247515 | Brady et al. | Aug 2018 | A1 |
| 20180255381 | Cella et al. | Sep 2018 | A1 |
| 20180278489 | Frackelton et al. | Sep 2018 | A1 |
| 20180279952 | Orron et al. | Oct 2018 | A1 |
| 20180281191 | Sinyavskiy et al. | Oct 2018 | A1 |
| 20180282633 | Van De Cotte et al. | Oct 2018 | A1 |
| 20180284093 | Brown et al. | Oct 2018 | A1 |
| 20180284741 | Cella et al. | Oct 2018 | A1 |
| 20180288158 | Nolan et al. | Oct 2018 | A1 |
| 20180292811 | Baseman et al. | Oct 2018 | A1 |
| 20180300610 | Pye et al. | Oct 2018 | A1 |
| 20180349508 | Bequet et al. | Dec 2018 | A1 |
| 20180364785 | Hu et al. | Dec 2018 | A1 |
| 20180375743 | Lee et al. | Dec 2018 | A1 |
| 20190020741 | Knaappila | Jan 2019 | A1 |
| 20190021039 | Sudarsan et al. | Jan 2019 | A1 |
| 20190024495 | Wise et al. | Jan 2019 | A1 |
| 20190025805 | Cella et al. | Jan 2019 | A1 |
| 20190025806 | Cella et al. | Jan 2019 | A1 |
| 20190025812 | Cella et al. | Jan 2019 | A1 |
| 20190025813 | Cella et al. | Jan 2019 | A1 |
| 20190033845 | Cella et al. | Jan 2019 | A1 |
| 20190033846 | Cella et al. | Jan 2019 | A1 |
| 20190033847 | Cella et al. | Jan 2019 | A1 |
| 20190033850 | B R et al. | Jan 2019 | A1 |
| 20190036946 | Ruvio et al. | Jan 2019 | A1 |
| 20190041840 | Cella et al. | Feb 2019 | A1 |
| 20190056107 | Desai et al. | Feb 2019 | A1 |
| 20190098377 | Kallus et al. | Mar 2019 | A1 |
| 20190137987 | Cella et al. | May 2019 | A1 |
| 20190137988 | Cella et al. | May 2019 | A1 |
| 20190140906 | Furuichi et al. | May 2019 | A1 |
| 20190163848 | McGranahan et al. | May 2019 | A1 |
| 20190170718 | Miresmailli et al. | Jun 2019 | A1 |
| 20190174207 | Cella et al. | Jun 2019 | A1 |
| 20190203653 | Bryan et al. | Jul 2019 | A1 |
| 20190204818 | Ogata et al. | Jul 2019 | A1 |
| 20190304037 | Kawano et al. | Oct 2019 | A1 |
| 20190324431 | Cella et al. | Oct 2019 | A1 |
| 20190326906 | Camacho Cardenas et al. | Oct 2019 | A1 |
| 20190339688 | Cella et al. | Nov 2019 | A1 |
| 20190349426 | Smith et al. | Nov 2019 | A1 |
| 20190354096 | Pal | Nov 2019 | A1 |
| 20200004561 | Kottler et al. | Jan 2020 | A1 |
| 20200034538 | Woodward et al. | Jan 2020 | A1 |
| 20200034638 | Brewington et al. | Jan 2020 | A1 |
| 20200045146 | Ito | Feb 2020 | A1 |
| 20200067789 | Khuti et al. | Feb 2020 | A1 |
| 20200103894 | Cella et al. | Apr 2020 | A1 |
| 20200133257 | Cella et al. | Apr 2020 | A1 |
| 20200150643 | Cella et al. | May 2020 | A1 |
| 20200150644 | Cella et al. | May 2020 | A1 |
| 20200150645 | Cella et al. | May 2020 | A1 |
| 20200201292 | Cella et al. | Jun 2020 | A1 |
| 20200244297 | Zalewski et al. | Jul 2020 | A1 |
| 20200284092 | Arkenstedt et al. | Sep 2020 | A1 |
| 20200301408 | Elbsat et al. | Sep 2020 | A1 |
| 20200304376 | Finkler et al. | Sep 2020 | A1 |
| 20200311559 | Chattopadhyay et al. | Oct 2020 | A1 |
| 20200359233 | Pergal | Nov 2020 | A1 |
| 20200387136 | Pöschmann et al. | Dec 2020 | A1 |
| 20200410590 | Regmi et al. | Dec 2020 | A1 |
| 20210085861 | Patel et al. | Mar 2021 | A1 |
| 20210096551 | Sayyarrodsari et al. | Apr 2021 | A1 |
| 20210109917 | Xiao et al. | Apr 2021 | A1 |
| 20210199534 | Bidwell et al. | Jul 2021 | A1 |
| Number | Date | Country |
|---|---|---|
| 2302000 | Sep 2000 | CA |
| 1284186 | Feb 2001 | CN |
| 1319967 | Oct 2001 | CN |
| 1716827 | Jan 2006 | CN |
| 2751314 | Jan 2006 | CN |
| 201138454 | Oct 2008 | CN |
| 101694577 | Apr 2010 | CN |
| 201945429 | Aug 2011 | CN |
| 102445604 | May 2012 | CN |
| 202539063 | Nov 2012 | CN |
| 202539064 | Nov 2012 | CN |
| 202583862 | Dec 2012 | CN |
| 102298364 | Mar 2013 | CN |
| 102970315 | Mar 2013 | CN |
| 103164516 | Jun 2013 | CN |
| 103220552 | Jul 2013 | CN |
| 203202640 | Sep 2013 | CN |
| 104142662 | Nov 2014 | CN |
| 104156831 | Nov 2014 | CN |
| 204178215 | Feb 2015 | CN |
| 104579552 | Apr 2015 | CN |
| 102762156 | Jun 2015 | CN |
| 104951292 | Sep 2015 | CN |
| 105427138 | Mar 2016 | CN |
| 205301926 | Jun 2016 | CN |
| 106855492 | Jun 2017 | CN |
| 29806131 | Jul 1998 | DE |
| 0897111 | Feb 1999 | EP |
| 1248216 | Oct 2002 | EP |
| 2801935 | Nov 2014 | EP |
| 2983056 | Feb 2016 | EP |
| S5913084 | Jan 1984 | JP |
| H06137164 | May 1994 | JP |
| H10152297 | Jun 1998 | JP |
| H1186178 | Mar 1999 | JP |
| H11118661 | Apr 1999 | JP |
| 2001133364 | May 2001 | JP |
| 2002155985 | May 2002 | JP |
| 2003337962 | Nov 2003 | JP |
| 2003345435 | Dec 2003 | JP |
| 2005346463 | Dec 2005 | JP |
| 2006522396 | Sep 2006 | JP |
| 2006302293 | Nov 2006 | JP |
| 2006338519 | Dec 2006 | JP |
| 2008232934 | Oct 2008 | JP |
| 2013073414 | Apr 2013 | JP |
| 2013250928 | Dec 2013 | JP |
| 2014170552 | Sep 2014 | JP |
| 2014203274 | Oct 2014 | JP |
| 2015128967 | Jul 2015 | JP |
| 20090103188 | Oct 2009 | KR |
| 20110009615 | Jan 2011 | KR |
| 20120111514 | Oct 2012 | KR |
| 9412917 | Jun 1994 | WO |
| 03090091 | Oct 2003 | WO |
| 2006014479 | Feb 2006 | WO |
| 2010138831 | Dec 2010 | WO |
| 2013123445 | Aug 2013 | WO |
| 2016068929 | May 2016 | WO |
| 2016137848 | Sep 2016 | WO |
| 2016182964 | Nov 2016 | WO |
| 2016187112 | Nov 2016 | WO |
| 2017098193 | Jun 2017 | WO |
| 2017136489 | Aug 2017 | WO |
| 2017196821 | Nov 2017 | WO |
| 2017198327 | Nov 2017 | WO |
| 2017196821 | Dec 2017 | WO |
| 2018142598 | Aug 2018 | WO |
| 2019028269 | Feb 2019 | WO |
| 2019094721 | May 2019 | WO |
| 2019094729 | May 2019 | WO |
| 2019094721 | Jun 2019 | WO |
| Entry |
|---|
| Mohak Shah, Big Data and the Internet of Things, Mar. 24, 2015, Arxiv ID: 1503.07092v1 [cs.CY], https://doi.org/10.48550/arXiv.1503.07092, pp. 1-33 (Year: 2015). |
| Ferry, et al., “Towards a Big Data Platform for Managing Machine Generated Data in the Cloud”, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), Emden, 2017, pp. 263-270. |
| Goundar, et al., Abstract of: “Real Time Condition Monitoring System for Industrial Motors”, 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), https://ieeexplore.ieee.org/document/7476232 (accessed Apr. 24, 2020), 2015, 1 page. |
| Goundar, et al., “Real Time Condition Monitoring System for Industrial Motors”, 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2015, 9 pages. |
| “International Application Serial No. PCT/US18/60034, International Preliminary Report on Patentability dated May 22, 2020”, Strong Force IoT Portfolio 2016, LLC, 17 pages. |
| “International Application Serial No. PCT/US18/60043, International Preliminary Report on Patentability dated May 22, 2020”, Strong Force IoT Portfolio 2016, LLC, 23 pages. |
| Rodrigues, et al., “RELOAD/CoAP Architecture with Resource Aggregation/Disaggregation Service”, IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Workshop: From M2M Communications to Internet of Things, 2016, 6 pages. |
| “European Application Serial No. 18840994.0 , Extended European Search Report dated Mar. 29, 2021”, Strong Force IoT Portfolio 2016, LLC, 13 pages. |
| Borge, S.B. , et al., “Multiple Mobile Nodes for Efficient Data Collection from Clusters in Wireless Sensor Network”, 2014 IEEE Global Conference on Wireless Computing and Networking, pp. 153-156. |
| Farnahm, Tim , “Proactive wireless sensor network for industrial IoT”, 2017 IEEE International Conference on Communications (ICC), 2017, 6 pages. |
| Google , “Google Search History”, Jul. 13, 2021, 1 page. |
| Google , “Google Search History”, Jul. 12, 2021, 1 page. |
| Google , “Google Search History”, Jul. 21, 2021, 1 page. |
| IEEE , “IEEE Xplore Search Results”, Jul. 10, 2021, 1 page. |
| Kim, Hyung-Won , et al., “Low Power Routing and Channel Allocation of Wireless Video Sensor Networks Using Wireless Link Utilization”, Ad Hoc & Sensor Wireless Networks, vol. 30, 2016, pp. 83-112. |
| Lee, Jay , et al., “Industrial Big Data Analytics and Cyber-Physical Systems for Future Maintenance & Service Innovation”, Procedia CIRP 38, 2015, pp. 3-7. |
| Linnenberg, Tobias , et al., “A market-based multi-agent-system for decentralized power and grid control”, Sep. 1, 2011, IEEE, 2011, pp. 1-8. |
| “Maxim High-Current, 25Q, SPDT, CMOS Analog Switches”, 2007, 12 pages. |
| Bouchoucha, et al., “Distributed Estimation Based on Observations Prediction in Wireless Sensor Networks”, IEEE Signal Processing Letters, vol. 22, No. 10, 2015, pp. 1530-1533. |
| Chaudhary, et al., “Energy Efficient Techniques for Data aggregation and collection in WSN”, International Journal of Computer Science, Engineering and Applications (IJCSEA) vol. 2, No. 4, 11 pages. |
| Dagar, et al., “Data Aggregation in Wireless Sensor Network: A Survey”, International Journal of Information and Computation Technology. ISSN 0974-2239 vol. 3, No. 3, 2013, pp. 167-174. |
| Di Maio, et al., “Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods”, IEEE Transactions on Reliability, vol. 62, No. 4, 2013, pp. 833-845. |
| Dimakis, et al., “Network Coding for Distributed Storage System”, IEEE Transactions on Information Theory, vol. 56, No. 9, 2010, 13 pages. |
| Gelenbe, et al., “Abstract of Gelenbe”, Oct. 28, 2008, pp. 1. |
| Gelenbe, et al., “Adaptive QoS Routing for Significant Events in Wireless Sensor Networks”, 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, Atlanta, GA,, Oct. 28, 2008, pp. 410-415. |
| Gelenbe, “Users and Services in Intelligent Networks”, IEE Proc. Intell. Transp. Syst., vol. 153, No. 3, 2006, pp. 213-220. |
| Kreibich, et al., “Quality-Based Multiple-Sensor Fusion in an Industrial Wireless Sensor Network for MCM”, Sep. 2014, IEEE Transaction on Industrial Electronics, vol. 61, No. 9,, 2014, pp. 4903-4911. |
| Ngai, et al., “Information-Aware Traffic Reduction for Wireless Sensor Networks”, 2009 IEEE 34th Conference on Local Computer Networks (LCN 2009), Zurich, Switzerland, 2009, pp. 451-458. |
| Niazi, et al., “A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments”, Feb. 2011, IEEE Sensors Journal, vol. 11, No. 2, 2011, pp. 404-412. |
| Orfanus, et al., “An Approach for Systematic Design of Emergent Self-Organization in Wireless Sensor Networks”, 2009 IEEE, 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009, pp. 92-98. |
| Pereira, et al., “A New Alternative Real-Time Method to Monitoring Dough Behavior During Processing Using Wireless Sensor”, International Journal of Food Engineering, vol. 9, Issue 4, 2013, pp. 505-509. |
| Saavedra, et al., “Vibration analysis of rotors for the identification of shaft misalignment Part 2: experimental validation”, Instn Meeh. Engrs vol. 218 Part C: J. Mechanical Engineering Science, 2004, 13 pages. |
| Soleimani, et al., “Abstract of Soleimani Reference”, 2016, 1 page. |
| Soleimani, et al., “RF Channel Modelling and Multi-Hop Routing for Wireless Sensor Networks Located on Oil Rigs”, IET Wireless Sensor Systems, vol. 6, Issue 5, 2016, pp. 173-179. |
| Sorensen, “Sigma-Delta Conversion Used for Motor Control”, Analog Devices technical articl, 2015, 6 pages. |
| “International Application Serial No. PCT/US17/31721, International Search Report and Written Opinion dated Sep. 11, 2017”, 23 pages. |
| “International Application Serial No. PCT/US18/45036, International Preliminary Report on Patentability dated Feb. 13, 2020”, Strong Force IoT Portfolio 2016, LLC, 164 pages. |
| “International Application Serial No. PCT/US18/45036, International Search Report and Written Opinion dated Mar. 21, 2019”, Strong Force IoT Portfolio 2016, LLC, 186 pages. |
| “International Application Serial No. PCT/US18/45036, Invitation to Pay Additional Fees and, Where Applicable, Protest Fee dated Jan. 14, 2019”, Strong Force Iot Portfolio 2016, LLC, 7 pages. |
| “International Application Serial No. PCT/US18/60034, International Search Report and Written Opinion dated May 16, 2019”, Strong Force IoT Portfolio 2016, LLC, 23 pages. |
| “International Application Serial No. PCT/US18/60034, Invitation to Pay Additional Fees and, Where Applicable, Protest Fee dated Mar. 26, 2019”, Strong Force IoT Portfolio 2016, LLC, 7 pages. |
| “International Application Serial No. PCT/US18/60043, Invitation to Pay Additional Fees and, Where Applicable, Protest Fee dated Feb. 13, 2019”, Strong Force IoT Portfolio 2016, LLC, 7 pages. |
| “International Application Serial No. PCT/US2018/060043, International Search Report and Written Opinion dated Apr. 2, 2019”, Strong Force IoT Portfolio 2016, LLC, 29 pages. |
| Reinhardt, “Designing Sensor Networks for Smart Spaces, Unified Interfacing and Energy-Efficient Communication Between Wireless Sensor and Actuator Nodes”, Vom Fachbereich Elektrotechnik und Informationstechnik der Technischen Universitat Darmstadt, 2011, 165 pages. |
| Azad, A.K.M. , et al., “Energy-Balanced Transmission Policies for Wireless Sensor Networks”, IEEE Transactions on Mobile Computing vol. 10, Issue 7, 2011, pp. 927-940. |
| Bal, Mert , “An Industrial Wireless Sensor Networks Framework for Production Monitoring”, IEEE, 2014, 6 pages. |
| Behere, Sagar , “A Generic Framework for Robot Motor Planning and Control”, Master's Thesis in Computer Science, at the Systems, Control and Robotics Master's Program Royal Institute of Technology, 2010, 71 pages. |
| Carey, W.M. , et al., “Abstract of Carey”, IEEE Xplore_Search_Results, 1977, 1 page. |
| Carey, W.M. , et al., “The Acoustic Background Noise of an Operating Liquid Metal Fast Breeder Reactor”, EBR-II, 1977, ICASSP 77, IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, 1977, pp. 393-396. |
| Chudasama, Shaktising R., et al., “Packet size optimization in wireless sensor network using cross-layer design approach”, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India,, 2014, pp. 2506-2511. |
| Dang, Quoc Khanh , et al., “Sensor Saturation Compensated Smoothing Algorithm for Inertial Sensor Based Motion Tracking”, Sensors 2014, 14, 2014, pp. 8167-8188. |
| Dasarathy, Belur V., “Industrial Applications of Multi-Sensor Multi-Source Information Fusion”, IEEE, 2000, 7 pages. |
| Geeta, D.D , et al., “Fault tolerance in wireless sensor network using hand-off and dynamic power adjustment approach”, Journal of Network and Computer Applications 36, 2013, pp. 1174-1185. |
| Kalore, Sushil Vilas , et al., “A Review on Efficient Routing Techniques in Wireless Sensor Networks”, 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) IMS Engineering College, Ghaziabad, India, 2015, 5 pages. |
| Koushanfar, Farinza , et al., “Fault Tolerance Techniques for Wireless Ad Hoc Sensor Networks”, IEEE, 2002, 6 pages. |
| Lincoln, Adrian , “What is operating deflection shape (ODS) analysis”, Prosig Noise & Vibration Blog, https://blog.prosig.com/2014/09/01 /what-is-operating-deflection-shape-ods-analysis/, Sep. 1, 2014, 2 pages. |
| Prandi, Luciano , et al., “A Low-Power 3-Axis Digital-Output MEMS Gyroscope with Single Drive and Multiplexed Angular Rate Readout”, ISSCC 2011 / Session 6 / Sensors & Energy Harvesting, 2011, 3 pages. |
| Raghunandan, G.H. , et al., “A Comparative Analysis of Routing Techniques for Wireless Sensor Networks”, Proceedings of the National Conference on Innovations in Emerging Technology—2011 Kongu Engineering College, Perundurai, Erode, Tamilnadu, lndia.17 & 18, 2011, pp. 17-22. |
| Roselin, J. , et al., “Energy Balanced Dynamic Deployment Optimization to Enhance Reliable Lifetime of Wireless Sensor Network International Journal of Engineering and Technology (IJET)”, vol. 5 No. 4, 2013, 11 pages. |
| Wikipedia Entry , “Petroleum Product”, (snapshot taken of Jan. 21, 2016 entry taken using Wayback machine; web.archive.org/web/ 20160121063510/https://en.wikipedia.org/wiki/Petroleum_product), 2016, 3 pages. |
| Abb , “Symphony Plus Condition Monitoring”, 2014, 8 pages. |
| Al-Karaki, Jamal N., et al., “Routing Techniques in Wireless Sensor Networks: A Survey”, downloaded from the internet “file:///C:/Users/olopez/Documents/e-Red 20Folder/16060107/Karaki.pdf”, 2004, pp. 6-28. |
| Chang, Yongping , et al., “ICA-ANN Method in Fault Diagnosis of Rotating Machinery”, 2012 IEEE, Published in 2016 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 236-240. |
| Gascon, Alberto , et al., “Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City”, Jan. 2022, pp. 1-23. |
| Han, Hong-Gui , et al., “A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network”, Applied Soft Computing, vol. 38, Jan. 2016, pp. 477-486. |
| Mulder, Jacob , et al., “Mobile Sensor Networks for Inspection Tasks in Harsh Industrial Environments”, 2010, pp. 1599-1618. |
| Rimell, Andrew N., et al., “Variation between manufacturers' declared vibration emission values and those measured under simulated workplace conditions for a range of hand-held power tools typically found in the construction industry”, International Journal of Industrial Ergonomics 38, 2008, pp. 661-675. |
| Saldivar, Alfredo Alan Flores , et al., “Abstract”, 1 page. |
| Saldivar, Alfredo Alan Flores , et al., “Self-Organizing Tool for Smart Design With Predictive Customer Needs and Wants to Realize Industry 4.0”, IEEE Congress on Evolutionary Computation (CEC), 2016, pp. 5317-5324. |
| Somasundara, A.A. , et al., “Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines”, 25th IEEE International Real-Time Systems Symposium, 2004, pp. 296-305. |
| Wang, Jie-Sheng , et al., “SOM Neural Network Fault Diagnosis Method of Polymerization Kettle Equipment Optimized by Improved PSO Algorithm”, The Scientific World Journal, 2012, 12 pages. |
| Zhang, Yingfeng , et al., “Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor”, IEEE Transactions on Industrial Informatics, vol. 13, No. 2, Apr. 2017, pp. 737-747. |
| 17796676.9 , “European Application Serial No. 17796676.9, Extended European Search Report dated Apr. 14, 2020”, Strong Force IoT Portfolio 2016, LLC, 11 pages. |
| Allen, Will , et al., “Flexible High-Speed Load Shedding Using a Crosspoint Switch”, 2006 Power Systems Conference: Advanced Metering, Protection, Control, Communication, and Distributed Resources, IEEE, Mar. 2006, pp. 14-17. |
| Burkle, Axel , et al., “Towards Autonomous Micro UAV Swarms”, J Intell Robot Syst, 2011, 15 pages. |
| Le, Duc-Hung , et al., “Managing On-Demand Sensing Resources in IoT Cloud Systems”, 2016 IEEE International Conference on Mobile Services, 2016, pp. 65-72. |
| Niyato, Dusit , et al., “Smart Data Pricing Models for the Internet of Things: A Bundling Strategy Approach”, IEEE Network, 2016, pp. 18-25. |
| Salahshoor, Karim , et al., Fault Detection and Diagnosis of an Industrial Steam Turbine Using a Distributed Configuration of Adaptive Neuro-Fuzzy Inference Systems, Feb. 4, 2011, pp. 1280-1293. |
| Xuelei, Zhao , et al., “Load Torque Observer for Cutting Motor of Roadheader Based on PLL”, Third Inti Conf, on Measuring Tech, and Mechatronics Automation, 2011, pp. 476-479. |
| Ali, Salman , et al., “Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring”, Sensors 2015, 2015, pp. 7172-7205. |
| Ancillotti, Emilio , et al., “The Role of Communication Systems in Smart Grids: Architectures, Technical Solutions and Research Challenges”, Computer Communications 36, 2013, pp. 1665-1697. |
| Marcu, Teodor , et al., “Miscellaneous Neural Networks Applied To Fault Detection and Isolation of An Evaporation Station”, IFAC Fault Detection, Supervision and Safety for Technical Processes, Budapest, Hungary, pp. 359-364. |
| Henriquez, Patricia , et al., “Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 44, No. 5, 2014, pp. 642-652. |
| Number | Date | Country | |
|---|---|---|---|
| 20190171187 A1 | Jun 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62584103 | Nov 2017 | US | |
| 62427141 | Nov 2016 | US | |
| 62412843 | Oct 2016 | US | |
| 62350672 | Jun 2016 | US | |
| 62333589 | May 2016 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2017/031721 | May 2017 | US |
| Child | 16185625 | US |
