Parts and equipment (“physical components”) used in industrial settings (e.g., food and beverage factories, power equipment facilities, ships, cranes, rail, etc.) change performance characteristics over time due to multiple factors including environmental conditions and operational wear. For example, common conditions that affect metallic objects over time are rust and corrosion. The degradation of physical components can occur over vastly different time frames (e.g., from weeks to decades), depending on the specific environmental conditions and the materials of the physical parts. As such, it is difficult to precisely identify the amount of degradation of a given component in an industrial setting, as data for making such judgements is not readily available. That said, robust identification of the degradation of physical components is important to the efficient operation of an industrial plant as incorrect identification can result in unnecessary replacement of physical components (e.g., physical components that have a significant amount of useful life remaining) and/or failure to take corrective actions (e.g., replacement or repair) for physical components that are likely to become inoperative or less efficient in the near term.
In one aspect, the present disclosure provides a system. The system includes circuitry (e.g., components, elements, subsystems, etc.) configured to apply an accelerated degradation process to a physical component of an industrial plant. Additionally, the circuitry of the system is configured to obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation, wherein the measurement data is usable to train a neural network to identify a phase of degradation of another physical component.
In another aspect, the present disclosure provides a method. The method includes applying, by a system for producing training data, an accelerated degradation process to a physical component of an industrial plant. Additionally, the method includes obtaining, by the system, measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation, wherein the measurement data is usable to train a neural network to identify a phase of degradation of another physical component.
In yet another aspect, the present disclosure provides one or more machine-readable storage media having a plurality of instructions stored thereon that, in response to being executed, cause a system to apply an accelerated degradation process to a physical component of an industrial plant. Additionally, the instructions cause the system to obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation, wherein the measurement data is usable to train a neural network to identify a phase of degradation of another physical component.
The concepts described herein are illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. Where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements. The detailed description particularly refers to the accompanying figures in which:
While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.
References in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Additionally, it should be appreciated that items included in a list in the form of “at least one A, B, and C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly, items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).
The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).
In the drawings, some structural or method features may be shown in specific arrangements and/or orderings. However, it should be appreciated that such specific arrangements and/or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, may not be included or may be combined with other features.
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The degradation analysis compute device 160, in some embodiments, may supplement the measurement data with simulated measurement data. That is, in some embodiments, the degradation analysis compute device 160 may determine, from the measurement data (e.g., the measurements obtained from degrading the physical components 140 in the degradation chamber 110), a model that describes the degradation of a physical component over time (e.g., under specified conditions) and producing, with the model, additional measurement data (e.g., visual characteristic data, such as images, etc.) that was not actually measured using the robot 130 and the degradation chamber 110. As such, the system 100 rapidly produces high quality data usable for training machine learning models (e.g., executed by the degradation identification compute device 170) to accurately and precisely determine the phase (e.g., amount) of degradation of a given physical component and facilitate a determination of whether the physical component should be replaced or repaired (e.g., based on its corresponding performance characteristics at the phase of degradation).
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The degradation chamber 110, in the illustrative embodiment, includes a control device 112, degradation devices 114, 116, and sensors 118, 120. While two degradation devices 114, 116 and two sensors 118, 120 are shown in
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The compute engine 210 may be embodied as any type of device or collection of devices capable of performing various compute functions described below. In some embodiments, the compute engine 210 may be embodied as a single device such as an integrated circuit, an embedded system, a field-programmable gate array (FPGA), a system-on-a-chip (SOC), or other integrated system or device. Additionally, in some embodiments, the compute engine 210 includes or is embodied as a processor 212 and a memory 214. The processor 212 may be embodied as any type of processor capable of performing the functions described herein. For example, the processor 212 may be embodied as a microcontroller, a single or multi-core processor(s), or other processor or processing/controlling circuit. In some embodiments, the processor 212 may be embodied as, include, or be coupled to an FPGA, an application specific integrated circuit (ASIC), reconfigurable hardware or hardware circuitry, or other specialized hardware to facilitate performance of the functions described herein.
The main memory 214 may be embodied as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory or data storage capable of performing the functions described herein. Volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. In some embodiments, all or a portion of the main memory 214 may be integrated into the processor 212. In operation, the main memory 214 may store various software and data used during operation such as parameters for simulating a target environment to accelerate the degradation of physical components 140, parameters for measuring characteristics of the physical components 140 at various phases of degradation, measurements obtained at various phases of degradation of the physical components 140, models that describe the degradation of physical components, applications, programs, libraries, and drivers.
The compute engine 210 is communicatively coupled to other components of the degradation analysis compute device 160 via the I/O subsystem 216, which may be embodied as circuitry and/or components to facilitate input/output operations with the compute engine 210 (e.g., with the processor 212 and the main memory 214) and other components of the degradation analysis compute device 160. For example, the I/O subsystem 216 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, integrated sensor hubs, firmware devices, communication links (e.g., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate the input/output operations. In some embodiments, the I/O subsystem 216 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with one or more of the processor 212, the main memory 214, and other components of the degradation analysis compute device 160, into the compute engine 210.
The communication circuitry 218 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network 150 between the degradation analysis compute device 160 and another device (e.g., the robot 130, the degradation chamber 110, the degradation identification compute device 170, etc.). The communication circuitry 218 may be configured to use any one or more communication technology (e.g., wired or wireless communications) and associated protocols (e.g., Ethernet, Bluetooth®, Wi-Fi®, WiMAX, etc.) to effect such communication.
The illustrative communication circuitry 218 includes a network interface controller (NIC) 220. The NIC 220 may be embodied as one or more add-in-boards, daughter cards, network interface cards, controller chips, chipsets, or other devices that may be used by the degradation analysis compute device 160 to connect with another compute device (e.g., the robot 130, the degradation chamber 110, the degradation identification compute device 170, etc.). In some embodiments, the NIC 220 may be embodied as part of a system-on-a-chip (SoC) that includes one or more processors, or included on a multichip package that also contains one or more processors. In some embodiments, the NIC 220 may include a local processor (not shown) and/or a local memory (not shown) that are both local to the NIC 220. In such embodiments, the local processor of the NIC 220 may be capable of performing one or more of the functions of the compute engine 210 described herein. Additionally or alternatively, in such embodiments, the local memory of the NIC 220 may be integrated into one or more components of the degradation analysis compute device 160 at the board level, socket level, chip level, and/or other levels.
Each data storage device 222, may be embodied as any type of device configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage device. Each data storage device 222 may include a system partition that stores data and firmware code for the data storage device 222 and one or more operating system partitions that store data files and executables for operating systems. Though shown as a single unit, it should be understood that in some embodiments, the components of the degradation analysis compute device 160 may be disaggregated (e.g., located in different racks, different portions of a data center, etc.).
The robot 130, the degradation chamber 110, and the degradation identification compute device 170 may have components similar to those described in
In the illustrative embodiment, the devices 110, 130, 160, and 170 are in communication via a network 150, which may be embodied as any type of wired or wireless communication network, including global networks (e.g., the internet), local area networks (LANs) or wide area networks (WANs), cellular networks (e.g., Global System for Mobile Communications (GSM), 3G, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), etc.), a radio area network (RAN), digital subscriber line (DSL) networks, cable networks (e.g., coaxial networks, fiber networks, etc.), or any combination thereof.
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In block 308, the system 100 obtains (e.g., from a configuration file in the data storage 222 of the degradation analysis compute device 160, from a request received from another compute device, or another source) measurement parameter data indicative of instructions to measure characteristics of the physical components 140 at multiple phases of degradation. In doing so, and as indicated in block 310, the system 100 may obtain measurement parameter data to measure visual characteristics of the physical components 140 at different phases of degradation. Additionally, the system 100 may obtain measurement parameter data to measure performance characteristics (e.g., strength testing, cycle fatigue resistance, etc.) of the physical components 140 at different phases of degradation, as indicated in block 312. In block 314, the system 100 may obtain measurement parameter data that indicates the sensor(s) 136, 118, 120 to utilize to make the measurements. As indicated in block 316, the system 100 may obtain measurement parameter data that indicates one or more angles to measure from, a number of measurements to take, a resolution, light angle(s), a color spectrum to image in, light polarization, and/or other parameters defining how the measurements are to be taken. The system 100 may also obtain measurement parameter data that indicates one or more grasping locations (e.g., location(s) on a physical component 140 that are to be held by the robot 130), as indicated in block 318. In some embodiments, the system 100 may obtain a computer aided design (CAD) file of the physical component(s) that provides data indicative of the grasping locations.
As indicated in block 320, the system 100 obtains physical component(s) 140 (e.g., by placing one or more of the physical components 140 into the degradation chamber 110 with the robot 130). In doing so, and as indicated in block 322, the system may obtain multiple samples 142, 144 of the same type of physical component (e.g., multiple gearboxes of the same design). As indicated in block 324, rather than obtaining an entire physical component, the system 100 may obtain a representative subsection (e.g., a “coupon” of the material in the physical component that has the material to be degraded and measured). Subsequently, the method 300 advances to block 326 of
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In obtaining the measurements, the system 100 may obtain the measurement data with a robot (e.g., the robot 130) physically located inside the degradation chamber 110. That is, the robot 130 may enter the degradation chamber 110 to obtain the measurements of the characteristics (e.g., by grasping the physical component(s) 140, rotating them, and measuring their characteristics from different angles), as indicated in block 372. In other embodiments, and as indicated in block 374, the system 100 may obtain the measurement data by removing (e.g., with the robot 130) the physical component(s) 140 from the degradation chamber (e.g., after a defined amount of the degradation process has occurred), obtaining the measurements (e.g., from multiple angles), and potentially reinserting one or more of the physical components 140 back into the degradation chamber 110 for additional degradation. In obtaining the measurement data, the system 100 may perform non-destructive measurements (e.g., imaging the physical component(s) from different angles), as indicated in block 376. Further, the system 100 may perform destructive measurements (e.g., in measuring the performance characteristics of the physical component(s) 140), as indicated in block 378. That is, the system 100 may destroy samples of the physical component(s) 140 during the measurement process. As such, in the illustrative embodiment, the system 100 may initially begin the degradation and measurement process with multiple samples (e.g., copies) of a physical component 140 to compensate for the gradual loss of samples (e.g., due to destructive measurements) during the degradation and measuring process. Subsequently, the method 300 advances to block 380 of
Referring now to
As indicated in block 388, the system 100 (e.g., the degradation analysis compute device 160) may utilize a symbolic regression engine to identify correlations in the development (e.g., growth, nucleation, etc.) of the features (e.g., features extracted in block 386) as a function of time (e.g., throughout the progress of degradation process). Furthermore, in some embodiments, the system 100 (e.g., the degradation analysis compute device 160) may incorporate, into the symbolic regression engine, one or more known (e.g., previously defined) equations that describe a degradation process (e.g., an equation that describes the changes in the features due to rusting), as indicated in block 390. As indicated in block 392, in some embodiments, the system 100 (e.g., the degradation analysis compute device 160) may determine a degradation model for different local geometries of a physical component 140. For example, and as indicated in block 394, the degradation analysis compute device 160 may determine a degradation model for different local geometries including raised features or inset features of a physical component 140, as those local geometries may alter the changes in features that would otherwise occur on a flat surface of the physical component 140. As indicated in block 396, the system 100 (e.g., the degradation analysis compute device 160) produces simulated measurement data using the degradation model that was determined in block 384 (e.g., additional measurements for phases that are represented in the measurement data from the robot 130 and the degradation chamber 110, measurement data for phases that were not actually measured using the robot 130 and the degradation chamber 110, etc.).
In some embodiments, the system 100 may train a neural network using the measurement data (e.g. from the robot 130 and the degradation chamber 110) and produce the simulated measurement data with the trained neural network, as indicated in blocks 398 and 400. In some embodiments, the system 100 (e.g., the degradation analysis compute device 160) may produce the simulated measurement data (e.g., including ray traced images of various phases of degradation) with one or more generative adversarial networks (GANs), as indicated in block 402. Subsequently, the method 300, in the illustrative embodiment, advances to block 404 of
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Subsequently, the system 100 (e.g., the degradation identification compute device 170, utilizing the trained neural network produced by the degradation analysis compute device in block 404) identifies phases of degradation of physical components (e.g., in an industrial setting) using the trained neural network, as indicated in block 410. Furthermore, a user of the system 100 (e.g., of the degradation identification compute device 170) may take corrective action (e.g., replacement or repair) for a physical component having a phase of degradation that satisfies predefined criteria (e.g., is at or beyond a predefined phase of degradation, has a performance characteristic, such as a strength, that is less than a predefined threshold, etc.), as indicated in block 412. The method 300 may, in some embodiments, loop back to block 302 of
While certain illustrative embodiments have been described in detail in the drawings and the foregoing description, such an illustration and description is to be considered as exemplary and not restrictive in character, it being understood that only illustrative embodiments have been shown and described and that all changes and modifications that come within the spirit of the disclosure are desired to be protected. There exist a plurality of advantages of the present disclosure arising from the various features of the apparatus, systems, and methods described herein. It will be noted that alternative embodiments of the apparatus, systems, and methods of the present disclosure may not include all of the features described, yet still benefit from at least some of the advantages of such features. Those of ordinary skill in the art may readily devise their own implementations of the apparatus, systems, and methods that incorporate one or more of the features of the present disclosure.