Intelligent safety motor control center (ISMCC)

Information

  • Patent Grant
  • 11687053
  • Patent Number
    11,687,053
  • Date Filed
    Monday, March 8, 2021
    3 years ago
  • Date Issued
    Tuesday, June 27, 2023
    a year ago
Abstract
Some implementations provides a system to implement a safety control at an oil and gas facility, the system comprising: one or more motor control centers, each comprising a network interface, and a programmable logic controller (PLC), wherein each motor control center is configured to monitor and control one or more field devices coupled thereto, and wherein a plurality of field devices are dispersed at the oil and gas facility; and a safety instrumented system (SIS) in communication with the one or more motor control centers through the network interface thereof, wherein each motor control center is configured to communicate with the SIS without an interposing relay, and wherein the SIS comprises control elements configured to implement the safety control at the oil and gas facility based on communication with each motor control center through the network interface thereof.
Description
TECHNICAL FIELD

This disclosure generally relates to industrial process automation.


BACKGROUND

In the oil and gas industry, a production plant may span a geographic area in which a large number of devices may operate in tandem. These industrial devices are often monitored and controlled remotely and automatically to maintain the successful operation of the production plant.


SUMMARY

Some implementations provide a system to implement a safety control at an oil and gas facility, the system comprising: one or more motor control centers, each comprising a network interface, and a programmable logic controller (PLC), wherein each motor control center is configured to monitor and control one or more field devices coupled thereto, and wherein a plurality of field devices are dispersed at the oil and gas facility; and a safety instrumented system (SIS) in communication with the one or more motor control centers through the network interface thereof, wherein each motor control center is configured to communicate with the safety instrumented system without an interposing relay, and wherein the safety instrumented system comprises control elements configured to implement the safety control at the oil and gas facility based on communication with each motor control center through the network interface thereof.


Implementations may include one or more of the following features.


The network interface may be configured to accommodate at least one redundant communication path from each motor control center to the safety instrumented system. The network interface may be compliant with a safety standard for automatic protection of the field devices dispersed at the oil and gas facility. The safety standard may include: an International Electrotechnical Commission (IEC) 61508 standard, an IEC 61511 standard.


Each motor control center may include triplicate or 1oo2D (one-out-of-two with diagnostics) components such that each motor control center can meet a threshold level of fault tolerance. The threshold level of fault tolerance may be Safety Integrity Level (SIL) 3, and wherein each motor control center may be rated at SIL 3. Each motor control center and the safety instrumented system may be synchronized with an accuracy under 1 millisecond. Each motor control center may be configured to communicate with the one or more field device according to a communication protocol, wherein the communication protocol comprises one of: a Highway Addressable Remote Transducer (HART) protocol, a Foundation Fieldbus protocol, or a RS 485 protocol. Each motor control center may further include a computing module coupled to the PLC and configured to: receive, from the PLC, data encoding diagnostic information of the one or more field devices and measurements from the one or more field devices; and process the data using machine learning algorithms to predict a performance of the one or more field devices. The computing module may be further configured to store the data, and wherein the machine learning algorithms are trained based on the stored data.


In another aspect, some implementations may provide a method to implement a safety control of an industrial process running at an oil and gas facility, the method comprising: operating one or more motor control centers, each comprising a network interface, and a programmable logic controller (PLC), wherein each motor control center is configured to monitor and control one or more field devices coupled thereto, and wherein a plurality of field devices are dispersed at the oil and gas facility during the industrial process; and operating a safety instrumented system (SIS) in communication with the one or more motor control centers through the network interface thereof, wherein each motor control center is configured to communicate with the safety instrumented system without an interposing relay, and wherein the safety instrumented system comprises control elements configured to implement the safety control of the industrial process running at the oil and gas facility based on communication with each motor control center through the network interface thereof.


Implementations may include one or more of the following features.


The method may further include: establishing, through the network interface, at least one redundant communication path from each motor control center to the safety instrumented system. The network interface may be compliant with a safety standard for automatic protection of field devices operating at the oil and gas facility. The safety standard may include one of: an International Electrotechnical Commission (IEC) 61508 standard, an IEC 61511 standard.


Each motor control center may include triplicate or 1oo2D (one-out-of-two with diagnostics) components such that each motor control center can meet a threshold level of fault tolerance. The threshold level of fault tolerance may be Safety Integrity Level (SIL) 3, and wherein each motor control center may be rated at SIL 3. Each motor control center and the safety instrumented system may be synchronized with an accuracy under 1 millisecond. Each motor control center may be configured to communicate with the one or more field device according to a communication protocol, wherein the communication protocol comprises one of: a Highway Addressable Remote Transducer (HART) protocol, a Foundation Fieldbus protocol, or a RS 485 protocol. Each motor control center may further include a computing module coupled to the PLC and configured to: receive, from the PLC, data encoding diagnostic information of the one or more field devices and measurements from the one or more field devices; and process the data using machine learning algorithms to predict a performance of the one or more field devices. The computing module may be further configured to store the data, and wherein the machine learning algorithms are trained based on the stored data.


Implementations according to the present disclosure may be realized in computer implemented methods, hardware computing systems, and tangible computer readable media. For example, a system of one or more computers can be configured to perform particular actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation cause the system to perform the actions. One or more computer programs can be configured to perform particular actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.


The details of one or more implementations of the subject matter of this specification are set forth in the description, the claims, and the accompanying drawings. Other features, aspects, and advantages of the subject matter will become apparent from the description, the claims, and the accompanying drawings.





DESCRIPTION OF DRAWINGS


FIG. 1 is a diagram illustrating an example of an apparatus for implementing intelligent industrial control according to an implementation of the present disclosure.



FIG. 2 is a diagram illustrating a comparison between a conventional apparatus and an intelligent apparatus according to an implementation of the present disclosure.



FIG. 3 is an example of a flow chart according to an implementation of the present disclosure.



FIG. 4 is a block diagram illustrating an example of a computer system used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures, according to an implementation of the present disclosure.





Like reference numbers and designations in the various drawings indicate like elements.


DETAILED DESCRIPTION

In the oil and gas industry, a production site can operate a large number of physical devices including, for example, motors, pumps, monitoring devices, robotics devices, storage devices. The equipment can span a relatively large geographic area, for example, at an oil field. Generally, the equipment in the field can consume energy, for example, in the form of electricity. A networked control system can be used to monitor and control the function of the equipment in the field. In a conventional control system, all signals between the motor control center and safety systems are hardwired through an interposing relay cabinet installed within substations.


Implementations described by the present disclosure include an integrated apparatus platform, centrally or distributed deployed, that can continuously operate in real-time mode to monitor and control the safe operations of oil field equipment. Here, the safe operation refers to fault tolerance and redundancy in the presence of hardware failure. The implementations can include an intelligent safety motor control center (ISMCC) that proactively improves system architecture by, for example, eliminating the interposing relay cabinet for interfacing the signals to the safety instrumented system. As such, the implementations can reduce system size, complexity and cost. Implementations can incorporate intrinsic system and network design that is Safety Integrity Level (SIL) 3 rated and compliant with the IEC61508/IEC61511 standards and specifications. Some implementations may be equipped with a program logic controller (PLC), power supply, and communication devices. In some cases, the PLC is directly connected with a local computer to have a local human machine interface. Some implementations can incorporate an ISMCC network system interface to the safety instrumented system (SIS) based on single mode or multimode fiber cable strands. In these implementations, the ISMCC may connect directly to both motor control center and SIS. The interconnection can be also achieved using a switched computer network.


The terminology used in the present disclosure includes the following terms.


The term “machine learning analytics” refers to the use of machine learning and applied statistics to predict unknown conditions based on the available data. Two general areas that fall under machine learning analytics are classification and regression. While classification refers to the prediction of categorical values, regression connotes the prediction of continuous numerical values. One machine learning implementation is also known as “supervised learning” where the “correct” target or y values are available. For illustration, the goal of some implementations is to learn from the available data to predict the unknown values with some defined error metrics. In supervised learning, for example, there are a set of known predictors (features) x1, x2, . . . , xm which are known to the system as well as the target values y1, y2, . . . yn, which are to be inferred. The system's objective is to train a machine learning model to predict new target values y1, y2, . . . , yn by observing new features.


The implementations can employ a variety of machine learning algorithms. For classification, examples of prediction algorithms can include, logistic regression, decision trees, nearest neighbor, support vector machines, K-means clustering, boosting, and neural networks. For regression, examples of predication algorithms can include least squares regression, Lasso, and others. The performance of an algorithm can depend on a number factors, such as the selected set of features, training/validation method and hyper-parameters tuning. As such, machine learning analytics can manifest as an iterative approach of knowledge finding that includes trial and error. An iterative approach can iteratively modify data preprocessing and model parameters until the result achieves the desired properties.


Referring to FIG. 1, diagram 100 shows an apparatus platform that integrates an intelligent safety motor safety control center (ISMCC) 101. The ISMCC 101 can be centrally or distributed deployed in the system for implementing safety control of an industrial process running at the oil and gas facility. In some cases, the ISMCC 101 can have an interface to a local computer (103). The ISMCC 101 can include an interface to a safety integrity level (SIL) rated high speed optical network 104. Through this network 104, the ISMCC 101 can communicate with a safety instrumented system (SIS) 105. The interface on ISMCC 101 may be configured to support at least one redundant communication path from each motor control center to the safety instrumented system. In various implementations, the ISMCC 101 can proactively optimize system architecture by, for example, eliminating interposing relay cabinet for interfacing the signals from the ISMCC to the safety instrumented system 105. Indeed, implementations can incorporate system and network design compliant with the IEC61508/IEC61511 standards and specifications. The ISMCC 101 can continuously operate in real-time mode to monitor and control load 102.


Load 102 can include one or more field devices that consume electricity. Examples of the field devices can include pumps, motors, valves, monitoring devices, robotics devices, and storage devices. These field devices are industrial equipment that can be dispersed throughout the oil and gas facility. The field devices participate in an industrial process running at the oil and gas facility. As such, these filed devices can fail and the control system design of the present disclosure presents technical solutions to monitor, predict, and handle device failure without the use of an interposing relay cabinet, which can be bulky and costly. The field devices can generate measurement data characterizing an aspect of the industrial process (e.g., flow rate, air or fluid pressure). The field devices can also generate diagnostic information such as an indication of the operating status (e.g., full, empty, idle, overload, etc.).


ISMCC 101 can include programmable logic controller (PLC) 106, communication module 107, and power supply 108. The power supply is generally responsible for powering the local electronics on ISMCC 101. In some implementations, PLC 106 is equipped with a redundant Central Processing Unit (CPU) running at, for example, 1.9 GHZ or higher with scanning execution cycle configured from 50 msec with an increment of 5 msec for input/output (I/O) scan and execution of both application and diagnostics. As an illustration, PLC 106 may include a minimum of 1 GB RAM plus 8 MB FLASH memory for both processing and storing collected data. In these implementations, the redundant CPUs can read and write to a single memory and or a redundant memory with automated synchronization. In one case, ISMCC 101 may have a time synchronization based on a broadcast from a system server (e.g., a network time server) or a GPS time receiver to achieve a synchronization accuracy of under, for example, one millisecond. ISMCC 101 can support a local or remote programming interface via a laptop computer (e.g., local computer 103). In some implementations, ISMCC 101 can include triplicate or 1oo2D (one-out-of-two with diagnostics) components such that the ISMCC 101 can meet a fault tolerance level of safety integrity level (SIL) 3. The communications between ISMCC 101 and safety instrumented system 105 are implemented through redundant network interfaces. In the implementations, each time a communication is received, an acknowledgment is transmitted to the transmitter and an error check is performed on the received message to validate the integrity of the received message. Moreover, ISMCC 101 can interface with load 102 by reading data from and write data to field devices. These field devices can include analog or smart field devices, which can be wired or wireless devices. ISMCC 101 may communicate with the field devices (e.g., smart field devices) via a communication protocol such as a Highway Addressable Remote Transducer (HART) protocol, a Foundation Fieldbus protocol, or a RS 485 protocol. ISMCC 101 may have built-in protection circuit to protect against common transient surges of up to, for example, 500V in root mean square (RMS) value. ISMCC 101 can include a control console (or through local computer 103) to provide a programmable visual display to indicate a health status of the equipment, a progress status of an operation, or an alarm. ISMCC 101 can automatically store the alarms, as well as control and protection events through programming device. ISMCC 101 can include computing capabilities to display the status and diagnostic information without the use of external devices. The computing capabilities can also receive, from the PLC, data encoding diagnostic information of the one or more field devices and measurements from the one or more field devices; and locally process the data using machine learning algorithms to predict a performance of the one or more field devices. Here, the implementations may incorporate a local human computer interface for data displays and user interactions on, for example, local computer 103. The implementations can store diagnostic information and measurement data as logs. Some implementations can provide data management capabilities with respect to the diagnostic information and measurement data. Data analytics applications running machine learning algorithms can operate on the diagnostic information and measurement data. Some implementations may also train the machine learning algorithms based on the diagnostic information and measurement data. ISMCC 101 can also communicate the stored information to a remote device, for example, through a network interface. In these implementations, the stored information is not perishable in that a reset of ISMCC 101 may not cause the information to be deleted.


ISMCC 101 may also include communication module 107. The communication module 107 may include universal Ethernet redundant port interfaces capable of expanding to, for example, 8 ports each with a speed from 100 Mbps to 1000 Mbps. The interface can support multimode fiber, single mode fiber and RJ45 copper. In some cases, communication module 107 can convert a RS485 connection over copper, over fiber, or over Ethernet. In some cases, the communication module may be integrated with PLC 106. As explained, the communications between the ISMCC and the safety instrumented system are implemented through redundant network interfaces. Moreover, each time a message is received from a transmitter, an acknowledgement is transmitted back to the transmitter and an error check is performed on the received message to validate its integrity.



FIG. 2 is a diagram 200 showing an implementation of the present disclosure that eliminates an interposing relay between a conventional motor control center and the safety instrumented system. In more detail, a conventional motor and control center 207 may establish connections with interposing relays 206. The interposing relays 206 can be a cabinet loaded with interposing relays for multiple conventional motor control centers. By way of background, interposing relays can be used between mismatched sensors, controllers, and/or control devices. The interposing relays can protect, for example, controllers from overcurrent in the field. As illustrated, the communicating path (dashed lines) between the interposing relays 206 and conventional MCC 207 can accommodate one pair of each signal to provide redundancy. As illustrated, the interposing relays 206 can provide a communication path (dashed line) to SIL 3 rated safety instrumented system 203 and a separate communication path (dashed line) to control system 204. The two communication paths can provide redundancy to further complete another communication path between control system 204 and SIL 3 rated safety instrumented system 203. As illustrated, all signals between the conventional MCC 207 and the safety instrumented system 203 are hardwired through an interposing relay cabinet installed within substations. An operator interface 205 may allow an operator to access control system 204 to, for example, visualize diagnostic information from field devices, or measurement data from the industrial process being monitored.


As illustrated, in various implementations, interposing relays 206 are no longer needed for interfacing the signals between intelligent safety motor control center (ISMCC) 201 and safety instrumented system 201. The reduction in system size can save operating cost of the full configuration for monitoring and controlling the industrial process. Instead of using an interposing relay to interface the signals between mismatching systems, implementations can use network interfaces between equipment in the field and the control system for transmitting commands and receiving feedbacks. Indeed, the ISMCC is capable of driving the loads in the field through local circuits by obtaining commands and sending feedback through communication cards running on the network interfaces.



FIG. 3 illustrates a flow chart 300 according some implementations of the present disclosure. An example of a process may include operating one or more motor control centers (301), such as ISMCC 101, each including a network interface compliant with a functional safety standard of, for example, IEC61508/61511. Each motor control center is coupled to one or more field devices, such as valves, pumps, motors, robotics that are dispersed in a gas and oil facility spanning over a large geographic area. Each field device can generate diagnostic information about its operating status. Each field device can also obtain measurement data, such as pressure, flow rate, and volume. Each motor control center can communicate with the field devices via a communication protocol such as a HART protocol, a Foundation Fieldbus protocol, or a RS 485 protocol. Each motor control center can additionally include a programmable logic controller (PLC). The programmable logic controller can include a computing module configured to receive the diagnostic information and measurement data (304), and process the diagnostic information and measurement data to predict a performance of the field devices (305). The PLC may invoke machine learning algorithms, which can be trained based on the diagnostic information and measurement data. In some cases, the PLC can store the diagnostic information and measurement data (306).


As illustrated, the process may include operating a safety instrumented system (302). The process may then establish a communication between the safety instrumented system and the motor control center through the network interface compliant with the industrial safety standard IEC61508/61511 (303). In some implementations, the safety instrumented system can receive information from the motor control centers in real-time. In these implementations, the motor control center and the safety instrumented system are synchronized with an accuracy under 1 millisecond. The safety instrumented system can itself be rated SIL 3 in terms of fault tolerance. For example, the safety instrumented system can include triplicate components as redundant provisions.



FIG. 4 is a block diagram illustrating an example of a computer system 400 used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures, according to an implementation of the present disclosure. The illustrated computer 402 is intended to encompass any computing device such as a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, another computing device, or a combination of computing devices, including physical or virtual instances of the computing device, or a combination of physical or virtual instances of the computing device. Additionally, the computer 402 can comprise a computer that includes an input device, such as a keypad, keyboard, touch screen, another input device, or a combination of input devices that can accept user information, and an output device that conveys information associated with the operation of the computer 402, including digital data, visual, audio, another type of information, or a combination of types of information, on a graphical-type user interface (UI) (or GUI) or other UI.


The computer 402 can serve in a role in a computer system as a client, network component, a server, a database or another persistency, another role, or a combination of roles for performing the subject matter described in the present disclosure. The illustrated computer 402 is communicably coupled with a network 403. In some implementations, one or more components of the computer 402 can be configured to operate within an environment, including cloud-computing-based, local, global, another environment, or a combination of environments.


The computer 402 is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer 402 can also include or be communicably coupled with a server, including an application server, e-mail server, web server, caching server, streaming data server, another server, or a combination of servers.


The computer 402 can receive requests over network 403 (for example, from a client software application executing on another computer 402) and respond to the received requests by processing the received requests using a software application or a combination of software applications. In addition, requests can also be sent to the computer 402 from internal users, external or third-parties, or other entities, individuals, systems, or computers.


Each of the components of the computer 402 can communicate using a network 403 (or system bus). In some implementations, any or all of the components of the computer 402, including hardware, software, or a combination of hardware and software, can interface over the network 403 (or system bus) using an application programming interface (API) 412, a service layer 413, or a combination of the API 412 and service layer 413. The API 412 can include specifications for routines, data structures, and object classes. The API 412 can be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer 413 provides software services to the computer 402 or other components (whether illustrated or not) that are communicably coupled to the computer 402. The functionality of the computer 402 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 413, provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, another computing language, or a combination of computing languages providing data in extensible markup language (XML) format, another format, or a combination of formats. While illustrated as an integrated component of the computer 402, alternative implementations can illustrate the API 412 or the service layer 413 as stand-alone components in relation to other components of the computer 402 or other components (whether illustrated or not) that are communicably coupled to the computer 402. Moreover, any or all parts of the API 412 or the service layer 413 can be implemented as a child or a sub-module of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.


The computer 402 includes an interface 404. Although illustrated as a single interface 404 in FIG. 4, two or more interfaces 404 can be used according to particular needs, desires, or particular implementations of the computer 402. The interface 404 is used by the computer 402 for communicating with another computing system (whether illustrated or not) that is communicatively linked to the network 403 in a distributed environment. Generally, the interface 404 is operable to communicate with the network 403 and comprises logic encoded in software, hardware, or a combination of software and hardware. More specifically, the interface 404 can comprise software supporting one or more communication protocols associated with communications such that the network 403 or interface's hardware is operable to communicate physical signals within and outside of the illustrated computer 402.


The computer 402 includes a processor 405. Although illustrated as a single processor 405 in FIG. 4, two or more processors can be used according to particular needs, desires, or particular implementations of the computer 402. Generally, the processor 405 executes instructions and manipulates data to perform the operations of the computer 402 and any algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure.


The computer 402 also includes a database 406 that can hold data for the computer 402, another component communicatively linked to the network 403 (whether illustrated or not), or a combination of the computer 402 and another component. For example, database 406 can be an in-memory, conventional, or another type of database storing data consistent with the present disclosure. In some implementations, database 406 can be a combination of two or more different database types (for example, a hybrid in-memory and conventional database) according to particular needs, desires, or particular implementations of the computer 402 and the described functionality. Although illustrated as a single database 406 in FIG. 4, two or more databases of similar or differing types can be used according to particular needs, desires, or particular implementations of the computer 402 and the described functionality. While database 406 is illustrated as an integral component of the computer 402, in alternative implementations, database 406 can be external to the computer 402. As illustrated, the database 406 holds the previously described data 416 including, for example, multiple streams of data from various sources, such as measurement data and diagnostic information from the field devices coupled to each motor control center, as shown in FIG. 1.


The computer 402 also includes a memory 407 that can hold data for the computer 402, another component or components communicatively linked to the network 403 (whether illustrated or not), or a combination of the computer 402 and another component. Memory 407 can store any data consistent with the present disclosure. In some implementations, memory 407 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 402 and the described functionality. Although illustrated as a single memory 407 in FIG. 4, two or more memories 407 or similar or differing types can be used according to particular needs, desires, or particular implementations of the computer 402 and the described functionality. While memory 407 is illustrated as an integral component of the computer 402, in alternative implementations, memory 407 can be external to the computer 402.


The application 408 is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 402, particularly with respect to functionality described in the present disclosure. For example, application 408 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 408, the application 408 can be implemented as multiple applications 408 on the computer 402. In addition, although illustrated as integral to the computer 402, in alternative implementations, the application 408 can be external to the computer 402.


The computer 402 can also include a power supply 414. The power supply 414 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 414 can include power-conversion or management circuits (including recharging, standby, or another power management functionality). In some implementations, the power-supply 414 can include a power plug to allow the computer 402 to be plugged into a wall socket or another power source to, for example, power the computer 402 or recharge a rechargeable battery.


There can be any number of computers 402 associated with, or external to, a computer system containing computer 402, each computer 402 communicating over network 403. Further, the term “client,” “user,” or other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 402, or that one user can use multiple computers 402.


Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal, for example, a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums. Configuring one or more computers means that the one or more computers have installed hardware, firmware, or software (or combinations of hardware, firmware, and software) so that when the software is executed by the one or more computers, particular computing operations are performed.


The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),” “near(ly) real-time (NRT),” “quasi real-time,” or similar terms (as understood by one of ordinary skill in the art), means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second (s), or less than 5 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.


The terms “data processing apparatus,” “computer,” or “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware and encompass all kinds of apparatus, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also be, or further include special purpose logic circuitry, for example, a central processing unit (CPU), an FPGA (field programmable gate array), or an ASIC (application-specific integrated circuit). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with an operating system of some type, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, another operating system, or a combination of operating systems.


A computer program, which can also be referred to or described as a program, software, a software application, a unit, a module, a software module, a script, code, or other component can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including, for example, as a stand-alone program, module, component, or subroutine, for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, for example, files that store one or more modules, sub-programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.


While portions of the programs illustrated in the various figures can be illustrated as individual components, such as units or modules, that implement described features and functionality using various objects, methods, or other processes, the programs can instead include a number of sub-units, sub-modules, third-party services, components, libraries, and other components, as appropriate. Conversely, the features and functionality of various components can be combined into single components, as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.


Described methods, processes, or logic flows represent one or more examples of functionality consistent with the present disclosure and are not intended to limit the disclosure to the described or illustrated implementations, but to be accorded the widest scope consistent with described principles and features. The described methods, processes, or logic flows can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output data. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.


Computers for the execution of a computer program can be based on general or special purpose microprocessors, both, or another type of CPU. Generally, a CPU will receive instructions and data from and write to a memory. The essential elements of a computer are a CPU, for performing or executing instructions, and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to, receive data from or transfer data to, or both, one or more mass storage devices for storing data, for example, magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable memory storage device.


Non-transitory computer-readable media for storing computer program instructions and data can include all forms of media and memory devices, magnetic devices, magneto optical disks, and optical memory device. Memory devices include semiconductor memory devices, for example, random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Magnetic devices include, for example, tape, cartridges, cassettes, internal/removable disks. Optical memory devices include, for example, digital video disc (DVD), CD-ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY, and other optical memory technologies. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories storing dynamic information, or other appropriate information including any parameters, variables, algorithms, instructions, rules, constraints, or references. Additionally, the memory can include other appropriate data, such as logs, policies, security or access data, or reporting files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.


To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, for example, a CRT (cathode ray tube), LCD (liquid crystal display), LED (Light Emitting Diode), or plasma monitor, for displaying information to the user and a keyboard and a pointing device, for example, a mouse, trackball, or trackpad by which the user can provide input to the computer. Input can also be provided to the computer using a touchscreen, such as a tablet computer surface with pressure sensitivity, a multi-touch screen using capacitive or electric sensing, or another type of touchscreen. Other types of devices can be used to interact with the user. For example, feedback provided to the user can be any form of sensory feedback. Input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with the user by sending documents to and receiving documents from a client computing device that is used by the user.


The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including but not limited to, a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.


Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server, or that includes a front-end component, for example, a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication), for example, a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) using, for example, 802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with the present disclosure), all or a portion of the Internet, another communication network, or a combination of communication networks. The communication network can communicate with, for example, Internet Protocol (IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, data, or other information between networks addresses.


The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.


While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what can be claimed, but rather as descriptions of features that can be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any sub-combination. Moreover, although previously described features can be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination can be directed to a sub-combination or variation of a sub-combination.


Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations can be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) can be advantageous and performed as deemed appropriate.


Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.


Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system comprising a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.

Claims
  • 1. A system to implement a safety control at an oil and gas facility, the system comprising: one or more motor control centers, each comprising a network interface that is an optical network interface, and a programmable logic controller (PLC) that includes a computing module comprising a processor and at least one memory in communication with the processor,wherein each motor control center is configured to monitor and control one or more field devices coupled thereto,wherein a plurality of field devices are dispersed at the oil and gas facility, wherein the processor is configured to:receive data encoding diagnostic information of the one or more field devices and measurements from the one or more field devices, andprocess the data using machine learning algorithms to predict a performance of the one or more field devices; anda safety instrumented system (SIS) in communication with the one or more moto control centers solely through the optical network interface thereof,wherein each motor control center is configured to communicate with the safety instrumented system without an interposing relay located between each motor control center and the safety instrumented system,wherein the safety instrumented system comprises a computer processor configured to implement the safety control at the oil and gas facility based on communication with each motor control center through the network interface thereof,wherein each motor control center and the safety instrumented system are synchronized with an accuracy under 1 millisecond, andwherein each motor control center comprises triplicate or 1oo2D (one-out-of-two with diagnostics) components such that each motor control center is rated SIL (Safety Integrity Level) 3 for operating at a SIL 3 level of fault tolerance while communication between the SIS and each motor control center achieves redundancy with reduced cost and latency in the absence of the interposing relay.
  • 2. The system of claim 1, wherein the network interface is configured to accommodate at least one redundant communication path from each motor control center to the safety instrumented system.
  • 3. The system of claim 1, wherein the network interface is compliant with a safety standard for automatic protection of the field devices dispersed at the oil and gas facility.
  • 4. The system of claim 3, wherein the safety standard comprises one of: an International Electrotechnical Commission (IEC) 61508 standard, an IEC 61511 standard.
  • 5. The system of claim 1, wherein each motor control center is configured to communicate with the one or more field device according to a communication protocol, wherein the communication protocol comprises one of: a Highway Addressable Remote Transducer (HART) protocol, a Foundation Fieldbus protocol, or a RS 485 protocol.
  • 6. The system of claim 1, wherein the computing module is further configured to store the data at the at least one memory, and wherein the machine learning algorithms are trained based on the stored data.
  • 7. A method to implement a safety control of an industrial process running at an oil and gas facility, the method comprising: operating one or more motor control centers, each comprising that is an optical networkinterface, and a programmable logic controller (PLC) that includes a computing module comprising a processor and at least one memory in communication with the processor,wherein each motor control center is configured to monitor and control one or more field devices coupled thereto,wherein a plurality of field devices are dispersed at the oil and gas facility, wherein the processor is configured to:receive data encoding diagnostic information of the one or more field devices and measurements from the one or more field devices, andprocess the data using machine learning algorithms to predict a performance of the one or more field devices; andoperating a safety instrumented system (SIS) in communication with the one or more moto control centers solely through the optical network interface thereof,wherein each motor control center is configured to communicate with the safety instrumented system without an interposing relay located between each motor control center and the safety instrumented system,wherein the safety instrumented system comprises a computer processor configured to implement the safety control at the oil and gas facility based on communication with each motor control center through the network interface thereof,wherein each motor control center and the safety instrumented system are synchronized with an accuracy under 1 millisecond, andwherein each motor control center comprises triplicate or 1oo2D (one-out-of-two with diagnostics) components such that each motor control center is rated SIL (Safety Integrity Level) 3 for operating at a SIL 3 level of fault tolerance while communication between the SIS and each motor control center achieves redundancy with reduced cost and latency in the absence of the interposing relay.
  • 8. The method of claim 7, further comprising: establishing, through the network interface, at least one redundant communication path from each motor control center to the safety instrumented system.
  • 9. The method of claim 7, wherein the network interface is compliant with a safety standard for automatic protection of field devices operating at the oil and gas facility.
  • 10. The method of claim 9, wherein the safety standard comprises one of: an International Electrotechnical Commission (IEC) 61508 standard, an IEC 61511 standard.
  • 11. The method of claim 7, wherein each motor control center is configured to communicate with the one or more field device according to a communication protocol, wherein the communication protocol comprises one of: a Highway Addressable Remote Transducer (HART) protocol, a Foundation Fieldbus protocol, or a RS 485 protocol.
  • 12. The method of claim 7, wherein the computing module is further configured to store the data at the at least one memory, and wherein the machine learning algorithms are trained based on the stored data.
US Referenced Citations (205)
Number Name Date Kind
3104549 Humbert et al. Sep 1963 A
3316767 Liebert May 1967 A
3373608 Ketelsen Mar 1968 A
4051723 Head et al. Oct 1977 A
RE31186 Rosenweig Mar 1983 E
4517846 Harrison et al. May 1985 A
4757314 Aubin Jul 1988 A
4777833 Carpenter Oct 1988 A
4901018 Lew Feb 1990 A
4965996 Morris Sep 1990 A
5067345 Mougne Nov 1991 A
5090250 Wada Feb 1992 A
5164897 Clark Nov 1992 A
5259239 Gaisford Nov 1993 A
5392648 Robertson Feb 1995 A
5417118 Lew et al. May 1995 A
5586310 Sharman Dec 1996 A
5975204 Tubel et al. Nov 1999 A
6006831 Schlemmer et al. Dec 1999 A
6046685 Tubel Apr 2000 A
6085599 Feller Jul 2000 A
6106032 Och Aug 2000 A
6163257 Tracy Dec 2000 A
6237424 Salmasi et al. May 2001 B1
6356844 Thomas et al. Mar 2002 B2
6463807 Feller Oct 2002 B1
6626048 Dam Es et al. Aug 2003 B1
6747372 Gilbreth et al. Jun 2004 B2
6882904 Petrie et al. Apr 2005 B1
6920799 Schulz Jul 2005 B1
6950825 Chang et al. Sep 2005 B2
7015800 Lesesky et al. Mar 2006 B2
7259688 Hirsch et al. Aug 2007 B2
7265544 Keese Sep 2007 B2
7469188 Wee Dec 2008 B2
7478024 Gurpinar et al. Jan 2009 B2
7493140 Michmerhuizen et al. Feb 2009 B2
7536547 Van Den Tillaart May 2009 B2
7540202 Bier Jun 2009 B2
7574907 Maute Aug 2009 B2
7584165 Buchan Sep 2009 B2
7644290 Ransom et al. Jan 2010 B2
7653936 Oberst Jan 2010 B2
7739359 Millet et al. Jun 2010 B1
7828065 Ross Nov 2010 B2
7933989 Barker et al. Apr 2011 B1
7940302 Mehrotra et al. May 2011 B2
8039991 Wakitani et al. Oct 2011 B2
8051722 Voigt et al. Nov 2011 B2
8102238 Golander et al. Jan 2012 B2
8195590 Storek Jun 2012 B1
8271212 Sai et al. Sep 2012 B2
8280635 Ella et al. Oct 2012 B2
8312320 Almadi Nov 2012 B2
8323392 Jones et al. Dec 2012 B2
8334775 Tapp et al. Dec 2012 B2
8359171 Bleys et al. Jan 2013 B2
8365250 Denny Jan 2013 B2
8365612 Izumi Feb 2013 B2
8543716 Rashidi Sep 2013 B1
8667091 Almadi Mar 2014 B2
8732106 Presgraves et al. May 2014 B1
8750513 Renkis Jun 2014 B2
8761911 Chapman et al. Jun 2014 B1
8792115 Harano Jul 2014 B2
8875379 Maute Nov 2014 B2
8884759 Oktem et al. Nov 2014 B2
8887241 Britton et al. Nov 2014 B2
8972742 Troncoso Pastoriza et al. Mar 2015 B2
9147174 Glickman et al. Sep 2015 B2
9208676 Fadell et al. Dec 2015 B2
9210179 Mevec et al. Dec 2015 B2
9396599 Malhotra Jul 2016 B1
9467472 Weiner et al. Oct 2016 B2
9699768 Werb Jul 2017 B2
9760075 Fisher-Rosemont Sep 2017 B2
10088840 Dorval et al. Oct 2018 B2
10330511 Alkhabbaz et al. Jun 2019 B2
10462884 Jayawardena et al. Oct 2019 B2
10466722 Mortensen et al. Nov 2019 B2
10514415 Jayawardena et al. Dec 2019 B2
10551047 Treible, Jr. et al. Feb 2020 B2
10897398 Al-Yousef et al. Jan 2021 B2
20020152053 Roy et al. Oct 2002 A1
20030117298 Seely Jun 2003 A1
20040045368 Schoeb Mar 2004 A1
20040098592 Taki May 2004 A1
20040188710 Koren et al. Sep 2004 A1
20040262008 Deans Dec 2004 A1
20050015624 Ginter et al. Jan 2005 A1
20050184084 Wells Aug 2005 A1
20050193832 Tombs et al. Sep 2005 A1
20050228683 Saylor et al. Oct 2005 A1
20060032547 Rossi Feb 2006 A1
20060085174 Hemanthkumar Apr 2006 A1
20060086497 Ohmer et al. Apr 2006 A1
20060107061 Holovacs May 2006 A1
20070018009 Choi et al. Jan 2007 A1
20070126576 Script et al. Jun 2007 A1
20070163359 Nielsen Jul 2007 A1
20070193834 Pai Aug 2007 A1
20070198223 Ella et al. Aug 2007 A1
20080061984 Breed et al. Mar 2008 A1
20080109883 Hernoud et al. May 2008 A1
20080109889 Bartels et al. May 2008 A1
20080139195 Marsyla et al. Jun 2008 A1
20080228908 Link Sep 2008 A1
20080251260 Ross et al. Oct 2008 A1
20080274766 Pratt et al. Nov 2008 A1
20090012631 Fuller Jan 2009 A1
20090037607 Farinacci et al. Feb 2009 A1
20090089108 Angell et al. Apr 2009 A1
20090141896 McCown Jun 2009 A1
20090210081 Sustaeta Aug 2009 A1
20090224930 Burza Sep 2009 A1
20100097205 Script Apr 2010 A1
20100228584 Nash Sep 2010 A1
20100231410 Seisenberger Sep 2010 A1
20100292857 Bose et al. Nov 2010 A1
20110066454 Rosauer et al. Mar 2011 A1
20110071963 Piovesan et al. Mar 2011 A1
20110074551 Higashionji Mar 2011 A1
20110178977 Drees Jul 2011 A1
20110181426 Bucciero et al. Jul 2011 A1
20110288692 Scott Nov 2011 A1
20110296377 Morozov et al. Dec 2011 A1
20120022700 Drees et al. Jan 2012 A1
20120031494 Lymberopoulos Feb 2012 A1
20120059634 Bouzarkouna Mar 2012 A1
20120060030 Lamb Mar 2012 A1
20120063354 Vanga et al. Mar 2012 A1
20120084400 Almadi et al. Apr 2012 A1
20120162423 Xiao Jun 2012 A1
20120172085 Vuppu Jul 2012 A1
20120307051 Welter Dec 2012 A1
20130085687 Danov et al. Apr 2013 A1
20130086650 Soundrapandian et al. Apr 2013 A1
20130088429 Yang Apr 2013 A1
20130103749 Weth et al. Apr 2013 A1
20130110411 Black et al. May 2013 A1
20130136597 Hansen et al. May 2013 A1
20130151020 Manninen et al. Jun 2013 A1
20130162405 Forster Jun 2013 A1
20130212259 Rankov et al. Aug 2013 A1
20130232338 Byres et al. Sep 2013 A1
20130247117 Yamada Sep 2013 A1
20130282641 Martin et al. Oct 2013 A1
20140019768 Pineau et al. Jan 2014 A1
20140046863 Gifford et al. Feb 2014 A1
20140089671 Logue Mar 2014 A1
20140118239 Phillips May 2014 A1
20140130874 Burlage May 2014 A1
20140139681 Jones, Jr. et al. May 2014 A1
20140150549 Rieger et al. Jun 2014 A1
20140156584 Motukuri et al. Jun 2014 A1
20140165182 Curry et al. Jun 2014 A1
20140230057 Berger Aug 2014 A1
20140240088 Robinette Aug 2014 A1
20140254799 Husted Sep 2014 A1
20140261791 Grabau et al. Sep 2014 A1
20140280953 Brzozowski et al. Sep 2014 A1
20140310059 Ellis Oct 2014 A1
20140337086 Asenjo et al. Nov 2014 A1
20140342373 Viovy et al. Nov 2014 A1
20140343717 Dorval et al. Nov 2014 A1
20150074023 Gu Mar 2015 A1
20150109104 Fadell Apr 2015 A1
20150116111 Foster Apr 2015 A1
20150195789 Yoon Jul 2015 A1
20150220321 Jung Aug 2015 A1
20160006745 Furuichi Jan 2016 A1
20160100437 Amstrong et al. Apr 2016 A1
20160123111 Kim May 2016 A1
20160206136 Storek Jul 2016 A1
20160234239 Knapp et al. Aug 2016 A1
20160259637 Kumar Sep 2016 A1
20160259647 Kim et al. Sep 2016 A1
20160379211 Hoyos Dec 2016 A1
20170031840 Cawse et al. Feb 2017 A1
20170034193 Schulman et al. Feb 2017 A1
20170053224 Duca et al. Feb 2017 A1
20170061715 Busch-Sorensen Mar 2017 A1
20170184659 Jayawardena et al. Jun 2017 A1
20170284191 Martin Oct 2017 A1
20170289812 Werb Oct 2017 A1
20170353491 Gukal Dec 2017 A1
20170356780 Smith et al. Dec 2017 A1
20180092331 Zuidhof Apr 2018 A1
20180156437 Freer et al. Jun 2018 A1
20190033852 Dorval et al. Jan 2019 A1
20190149894 Weatherhead et al. May 2019 A1
20190159322 Jayawardena et al. May 2019 A1
20190162330 Al-Ajmi May 2019 A1
20190234603 Treible, Jr. et al. Aug 2019 A1
20190306250 Kubo Oct 2019 A1
20190346839 Dorval et al. Nov 2019 A1
20190349254 Nolan et al. Nov 2019 A1
20200190931 Moen Jun 2020 A1
20200208510 Guijt Jul 2020 A1
20200239329 Patey Jul 2020 A1
20200252288 Al-Yousef et al. Aug 2020 A1
20200371514 Dorval et al. Nov 2020 A1
20210115782 Mujica Apr 2021 A1
20210404315 Parak Dec 2021 A1
20220162923 Signaroldi May 2022 A1
Foreign Referenced Citations (15)
Number Date Country
102520388 Jun 2012 CN
203322437 Dec 2013 CN
0770856 Sep 2003 EP
1612741 Jan 2006 EP
1832548 Dec 2007 EP
2396273 Dec 2011 EP
3196716 Jul 2017 EP
1493527 Nov 1977 GB
H 07152789 Jun 1995 JP
2014119266 Jun 2014 JP
WO 2009000283 Dec 2008 WO
WO 2015058134 Apr 2015 WO
WO 2016073267 May 2016 WO
WO 2016097998 Jun 2016 WO
WO 2018207123 Nov 2018 WO
Non-Patent Literature Citations (18)
Entry
AirMagnet Survey User Guide, NetAlly, Oct. 2019, 382 pages.
Almadi, “Intelligent Field Infrastructure Adoption: Approach and Best Practices,” SPE 150066, Society of Petroleum Engineers (SPE), presented at the SPE intelligent Energy International Conference, Mar. 27-29, 2012, 12 pages.
Boman, “IoT Technology to Reduce Need for Oil, Gas Workers Offshore,” Oct. 14, 2016, rigzone.com (online), retrieved from URL <https://www.rigzone.com/news/oil_gas/a/147044/iot_technology_to_reduce_need_for_oil_gas_workers_offshore/>, 5 pages.
Canaz, “Planar and Linear Feature-Based Registration of Terrestrial Laser Scans with Minimum Overlap Using Photogrammetric Data,” Masters Thesis, University of Calgary, Dec. 2012, 142 pages.
Cohen, “Reducing Business Surprises through Proactive, Real-Time Sensing and Alert Management,” EESR Workshop on End-to-End, Sense-and Respond Systems, Applications and Services, 2005, 6 pages.
Ekahau Pro, Ekahau.com, 2020, 2 pages.
Gokce et al., “Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles,” Sensors, 2015, 15:23805-23846, 42 pages.
Hale, “Evaluating safety management and culture interventions to improve safety: Effective intervention strategies,” Safety Science, Oct. 2010, 48:8 (1026-1035), 10 pages.
Huia et al., “Prediction of Safety Objective of an Enterprise using Fuzzy Neural Network,” International Symposium on Safety Science and Engineering in China, Procedia Engineering, 43: 162-167, 2012, 6 pages.
Husain et al., “Quantifying the Intelligent Field Added Values,” SPE 167439, Society of Petroleum Engineers (SPE), presented at the SPE Middle East Intelligent Energy Conference and Exhibition, Oct. 28-30, 2013, 9 pages.
Memon et al., “Distributed control system for process control using intelligent agents,” WSEAS Transactions on Systems, retrieved from URL <:https://www.researchgate.net/publication/270214069_Distributed_control_system_for_process_control_using_intelligent_agents>, retrieved on May 17, 2019, available on or before Mar. 1, 2006, 10 pages.
offshore-technology.com (online), “Take Control: Smart Valves Step Forward,” retrieved from URL <http://www.offshore-technology.com/features/feature2034/>, Jun. 18, 2008, 6 pages.
Petrie et al., “Chapter 1: Introduction to Laser Ranging, Profiling, and Scanning,” Topographic Laser Ranging and Scanning: Principles and Processing, 2008, 29 pages.
Petrie et al., “Chapter 3: Terrestrial Laser Scanners,” Topographic Laser Ranging and Scanning: Principles and Processing, 2009, 43 pages.
pyimagesearch.com [online], Rosebrock, “Find distance from camera to object/marker using Python and OpenCV,” Jan. 2015, retrieved on Oct. 21, 2020, retrieved from URL <https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/>, 109 pages.
Sample VisiWave Site Survey Report, VisiWave, Aug. 2012, 23 pages.
Storey, “Building a Maintenance Management Program for Valves,” Control Engineering, controleng.com (online), retrieved from URL <http://www.controleng.com/industry-news/single-article/building-a-maintenance-management-program-for-valves/20afd59f11c5dec4ec222cc79937e40b.html>, Apr. 17, 2014, 3 pages.
forumautomation.com [online], “Working of Interposing Relays in PLCs,” Jan. 3, 2018, retrieved on Mar. 5, 2021, retrieved from URL <https://forumautomation.com/t/working-of-interposingrelays-in-plcs/2918>, 3 pages.
Related Publications (1)
Number Date Country
20220283562 A1 Sep 2022 US