Machine Monitoring System

Abstract

A method for monitoring an industrial system, such as a pneumatic control system, may include monitoring a plurality of operating characteristics of a plurality of components of the system, recording a plurality of instances of the operating characteristics of each of the components, creating a mathematical model of the system from the plurality of instances of the operating characteristics, predicting a failure of one of the components by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. The model may model the system as a whole and / or include discrete modeling of any of the components.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of Indian Provisional Patent Application No. 202341066857 filed on October 5, 2023, the entire contents of which are incorporated herein by reference.

[0003] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0004] not applicable.

[0005] Appendix References

[0006] not applicable. Technical Field

[0007] The present disclosure relates generally to industrial systems, and more particularly to maintenance and monitoring of industrial systems. Background Art

[0008] Industrial systems often include many individual components, each of which is subject to failure through their own wear or damage caused by the failure of other components. Most industrial systems currently rely on scheduled maintenance to prevent such failures. However, scheduled maintenance may require the replacement of components that are functioning perfectly, and may not prevent the failure of other components, resulting in increased costs and downtime. For example, one cylinder of a pneumatic system may fail before a maintenance schedule requires its replacement or repair, while another cylinder may perform perfectly for a long time before a maintenance schedule requires its replacement.

[0009] Other techniques involve monitoring characteristics of systems and / or components and comparing those characteristics to thresholds in order to determine when to replace or repair a component. However, such techniques are purely reactive and cannot predict failures. Summary of the invention

[0010] Applicants have created new and useful devices, systems, and methods for monitoring industrial systems. In at least one embodiment, a method for monitoring an industrial system (such as a pneumatic control system) may include monitoring multiple operating characteristics of the system, recording multiple instances of the operating characteristics, creating a baseline or machine signature based on multiple instances of the operating characteristics, comparing trends in multiple instances of the operating characteristics to the baseline, predicting component failures based on the comparison, or any combination thereof. In at least one embodiment, the baseline may be or include a mathematical model of the pneumatic control system as a whole and / or a mathematical model of each monitored component of the pneumatic control system. In at least one embodiment, the baseline may include an average value of the operating characteristic. In at least one embodiment, the baseline may include a standard deviation of the operating characteristic. In at least one embodiment, the baseline may include one or more tolerances of the operating characteristic.

[0011] In at least one embodiment, the monitored component may include one or more pneumatic cylinders, one or more pneumatically controlled valves, another component of the system, or any combination thereof. In at least one embodiment, the operating characteristic may include opening speed, closing speed, cycle time, cycle count, flow rate, pressure, torque, another operating characteristic associated with a pneumatically controlled component, or any combination thereof.

[0012] The baseline or model may be fixed and / or change over time. In at least one embodiment, the baseline may be fixed once created. In at least one embodiment, the baseline includes fixed elements and moving elements. In at least one embodiment, the moving elements may be modified using subsequent instances of operating characteristics. In at least one embodiment, the baseline itself may be modified using subsequent instances of operating characteristics.

[0013] In at least one embodiment, predicting a component failure may include identifying a pattern in a trend, such as a pattern based on previous component failures. In at least one embodiment, the previous component failure may be a failure of or related to a component whose failure is predicted. In at least one embodiment, the previous component failure may be of or related to a different component other than the component whose failure is predicted. In at least one embodiment, the previous component failure may be of or related to a different component that previously performed the function of the component whose failure is predicted. In at least one embodiment, when predicting a component failure, a trend may be associated with one or more components, while a baseline may be associated with multiple components. In at least one embodiment, a trend may be a more recent trend when compared to an instance of an operating characteristic used to create a baseline or model. In at least one embodiment, a trend may include an instance of an operating characteristic used to create a baseline or model.

[0014] In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased cycle time of the pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased flow rate through the pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased flow rate to and / or through the pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased pressure associated with the pneumatically controlled component.

[0015] In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased cycle time of one pneumatically controlled component compared to another pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased flow rate through one pneumatically controlled component compared to another pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased flow rate to one pneumatically controlled component compared to another pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased pressure associated with one pneumatically controlled component compared to another pneumatically controlled component.

[0016] In at least one embodiment, a method for monitoring an industrial system, such as a pneumatic control system, may include monitoring a plurality of operating characteristics of a plurality of components of the system, recording a plurality of instances of the operating characteristics of each of the components, creating a mathematical model of the system based on the plurality of instances of the operating characteristics, predicting failure of one of the components by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components. In at least one embodiment, the model may model the system as a whole and / or include discrete modeling of any of the components.

[0017] In at least one embodiment, a method for monitoring an industrial system, such as a pneumatic control system, may include monitoring a plurality of operating characteristics of a plurality of components of the system, recording a plurality of instances of the operating characteristics of each of the components, creating a mathematical model of the system based on the plurality of instances of the operating characteristics, predicting the remaining useful life of one of the components by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components. In at least one embodiment, a method may include calculating the remaining useful life of a pneumatic cylinder. In at least one embodiment, the model may model the system as a whole and / or include discrete modeling of any of the components.

[0018] In at least one embodiment, a method for monitoring an industrial system (e.g., a pneumatic control system) may include monitoring multiple operating characteristics of multiple components of the system, recording multiple instances of the operating characteristics of each of the components, creating a mathematical model of the system based on the multiple instances of the operating characteristics, predicting the optimal pressure of the system to achieve production capacity by comparing trends in the multiple instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components. In at least one embodiment, the model may model the system as a whole and / or include discrete modeling of any of the components. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of one of many embodiments of a monitored industrial system according to the present disclosure.

[0020] Figure 2 is a schematic diagram of another of the various embodiments of a monitored industrial system according to the present disclosure.

[0021] Figure 3 is a graph of operating characteristics of an industrial system according to the present disclosure.

[0022] Figure 4 is another graph of operating characteristics of an industrial system according to the present disclosure.

[0023] Figure 5 is yet another graph of operating characteristics of an industrial system according to the present disclosure.

[0024] Figure 6 is a perspective view of one of many embodiments of a simplified industrial system being monitored in accordance with the present disclosure.

[0025] Figure 7 yes Figure 6 Front view of a portion of the system. DETAILED DESCRIPTION

[0026] The drawings described above and the following written descriptions of specific structures and functions are not presented for the purpose of limiting the scope of the invention that the applicant has invented or the scope of the appended claims. On the contrary, the drawings and written descriptions are provided to teach those skilled in the art to make and use the inventions for which patent protection is sought. Those skilled in the art will understand that, for the sake of clarity and understanding, not all features of the commercial embodiments of the present invention are described or shown. Those skilled in the art will also understand that the development of actual commercial embodiments incorporating various aspects of the present invention will require many implementation-specific decisions to achieve the developer's ultimate goal for the commercial embodiment. Such implementation-specific decisions may include and may not be limited to compliance with system-related, business-related, government-related and other constraints, which may vary from time to time depending on the specific implementation, location, and from time to time. Although the developer's efforts may be complex and time-consuming in an absolute sense, such efforts will be routine tasks for those skilled in the art who benefit from the present disclosure. It must be understood that the invention disclosed and taught herein is susceptible to many and various modifications and alternative forms.

[0027] Use singular terms, such as but not limited to " one (a) ", is not intended to limit the number of items.In addition, in written description, use relational terms, such as but not limited to " top ", " bottom ", " left ", " right ", " top ", " bottom ", " bottom ", " top ", " side " etc. for clear specific reference to the accompanying drawings, and are not intended to limit the scope of the present invention or appended claims.Term " comprises " and " such as " are illustrative and non-restrictive.Term " connect (couple) ", " connect (coupled) ", " connect (coupling) ", " coupler (coupler) " and similar terms are widely used in this article, and can include for fixing, combining, bonding, fastening, attaching, engaging, inserting therein, forming thereon or therein, communicating or otherwise for example mechanically, magnetically, electrically, chemically, operably, directly or indirectly with intermediate element any method or equipment associated together, and can further include but not limited to in a unified manner a functional component and another functional component are formed integrally.Connection can occur in any direction, including rotational occurrence. Furthermore, all parts and components of the present disclosure that can be physically embodied inherently include imaginary and real features, whether or not such features are explicitly described herein, including, but not limited to, features such as axes, ends, inner and outer surfaces, interior spaces, tops, bottoms, sides, boundaries, dimensions (e.g., height, length, width, thickness), mass, weight, volume, and density.

[0028] Any process flow chart discussed herein illustrates the possible implementation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart can represent a module, fragment or part of the code, which may include one or more executable instructions for implementing (one or more) specified logical functions. It should also be noted that in some embodiments, (one or more) functions marked in the box may not occur in the order depicted in the figure. For example, the blocks shown in succession can actually be executed substantially simultaneously. It will also be noted that each box illustrated in the flow chart can be implemented by a combination of a dedicated hardware-based system or dedicated hardware and computer instructions that performs a specified function or action.

[0029] Applicants have created new and useful devices, systems, and methods for monitoring industrial systems. In at least one embodiment, a method (such as a method for monitoring one or more industrial systems (e.g., a pneumatic control system)) may include monitoring multiple operating characteristics of (one or more) systems, recording multiple instances of the operating characteristics, creating (one or more) baselines or (one or more) machine signatures based on multiple instances of the operating characteristics, comparing (one or more) trends in multiple instances of the operating characteristics with (one or more) baselines, predicting component failure based on the (one or more) comparisons, or any combination thereof. In at least one embodiment, one or more baselines may be or include a mathematical model of the pneumatic control system as a whole and / or a mathematical model of one or more monitored components of the pneumatic control system. In at least one embodiment, one or more baselines may include one or more averages of one or more operating characteristics. In at least one embodiment, one or more baselines may include one or more standard deviations of one or more operating characteristics. In at least one embodiment, one or more baselines may include one or more tolerances of one or more operating characteristics.

[0030] Figure 1 is a schematic diagram of one of many embodiments of a monitored industrial system according to the present disclosure. Figure 2 is a schematic diagram of another of the various embodiments of a monitored industrial system according to the present disclosure. Figure 3 is a graph of operating characteristics of an industrial system according to the present disclosure. Figure 4 is another graph of operating characteristics of an industrial system according to the present disclosure. Figure 5 is yet another graph of operating characteristics of an industrial system according to the present disclosure. Figure 6 is a perspective view of one of many embodiments of a simplified industrial system being monitored in accordance with the present disclosure. Figure 7 yes Figure 6 Front view of a portion of the system. Figure 1-Figure 7 Describe in conjunction with each other.

[0031] In at least one embodiment, a method for monitoring an industrial system 100 (such as a pneumatic control system) according to the present disclosure may include monitoring multiple operating characteristics of the system 100, recording multiple instances of the operating characteristics, creating a baseline based on multiple instances of the operating characteristics, comparing trends in multiple instances of the operating characteristics with the baseline, predicting component failure based on the comparison, or any combination thereof. The industrial system 100 (such as a pneumatic control system) according to the present disclosure may include various components 102, such as one or more pneumatic cylinders, one or more pneumatically controlled valves, one or more other components of the system, or any combination thereof. Such cylinders may include linear actuated cylinders for moving or positioning objects and / or actuating other equipment, etc. Such valves may include process control valves, pilot operated valves, etc. In at least one embodiment, any and / or all of the components 102 may be monitored by one or more of the various sensors 106 (such as AVENTICS series AF2 flow sensors, AVENTICS series PE5 pressure sensors, etc.). It can be understood that after reading this disclosure, the components 102 of the industrial system 100 may fail, which may include failure to work fully or failure to perform satisfactorily.

[0032] In at least one embodiment, the components 102 of the industrial system 100 may be controlled by one or more controllers 104, such as programmable logic controllers. In at least one embodiment, the controllers 104 may monitor and / or record operational characteristics, such as in addition to controlling the components 102. In at least one embodiment, one or more artificial intelligence or machine learning modules 200 may be incorporated into the system 100, onboard the system 100, in communication with the system 100, or otherwise monitor one or more aspects of the system 100. In at least one embodiment, the modules 200 may monitor each monitored component 102a, 102b, 102c directly, do so through the controller 104, do so through one or more separate monitoring devices 202, or any combination thereof. In at least one embodiment, the modules 200 may include one or more analysis engines 204 that may perform component failure prediction. In at least one embodiment, the modules 200 may include one or more analysis databases 206 that may store or record operational characteristics. In at least one embodiment, the analysis engine 204 may read the operational characteristics through the controller 104 and / or the monitoring device 202 and store the operational characteristics within itself and / or in the analysis database 206. In at least one embodiment, analysis database 206 receives the operating characteristics directly from controller 104 and / or monitoring device 202, and analysis engine 204 accesses the operating characteristics in analysis database 206. In at least one embodiment, analysis engine 204 may report component failure predictions to controller 104, to another supervisory system, directly to an operator, or any combination thereof.

[0033] In at least one embodiment, the module 200 can communicate via an Rxi2 gateway, OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT), other communication technologies and protocols, or any combination thereof. In at least one embodiment, the monitoring device 202, the analysis engine 204, the analysis database 206, or any combination thereof can communicate with the controller 104 OPC-UA, MQTT, another communication protocol, or any combination thereof. In at least one embodiment, the real-time operating characteristics can be communicated from the sensor to the analysis engine 204 and / or the analysis database 206 using MQTT and / or an application programming interface (API). In at least one embodiment, the analysis engine 204 can report fault predictions using MQTT and / or API. In at least one embodiment, the analysis engine 204 can provide real-time and / or historical data to the controller 104, another supervisory system, directly to the operator, or any combination thereof.

[0034] It can be understood that after reading this disclosure, the components 102 of the industrial system 100 can have various operating characteristics. In at least one embodiment, the operating characteristics can include stroke length, opening speed, closing speed, cycle time, cycle count, flow rate, pressure, torque, another operating characteristic associated with the component 102, or any combination thereof. In at least one embodiment, the opening speed can indicate how long it takes for the component 102 to actuate (such as opening a valve). In at least one embodiment, the closing speed can indicate how long it takes for the component 102 to deactivate (such as closing a valve). In at least one embodiment, the cycle time can indicate how long it takes for the component 102 to fully cycle. Slower speeds can indicate potential failures, such as failures caused by lack of lubrication and / or accumulation of debris or corrosion. In at least one embodiment, the cycle count can indicate how many times the component 102 has cycled, which can indicate wear. In at least one embodiment, the flow rate can indicate the rate to or through the component 102. For example, a reduced flow rate can indicate a blockage and / or failure to fully open, while an increased flow rate can indicate a leak at or downstream of the component 102. In at least one embodiment, the pressure may indicate process control and / or instrument pressure. For example, a reduced pressure may indicate a leak at or downstream of the assembly 102, while an increased pressure may indicate a blockage and / or failure to fully open. In at least one embodiment, the torque may indicate the torque required to actuate, or open, or deactivate, or close the assembly 102, which may indicate a potential fault, such as a fault caused by a lack of lubrication and / or accumulation of debris or corrosion.

[0035] In an ideal situation, each time component 102 is actuated, the associated speed, flow rate, pressure, etc. are the same. However, in industrial system 100, this is not typically the case. Instead, these characteristics may vary from one activation to the next. However, not all changes necessarily indicate a fault or an impending fault. Therefore, in at least one embodiment, there may be a range of normal operating characteristics. These normal operating characteristics may be learned over time by module 200. In at least one embodiment, the baseline may be or include a mathematical model of the system 100 as a whole and / or a mathematical model of each monitored component of the system 100. In at least one embodiment, the baseline may include an average value of the operating characteristic. In at least one embodiment, the baseline may include a standard deviation of the operating characteristic. In at least one embodiment, the baseline may include one or more tolerances of the operating characteristic.

[0036] In at least one embodiment, module 200 can create and / or modify a baseline and / or model based on an operating characteristic that can be learned over time or receiving multiple instances of an operating characteristic (such as when system 100 is operating normally). A baseline or model can be fixed and / or change over time. In at least one embodiment, a baseline can be fixed once created. In at least one embodiment, a baseline includes fixed elements and moving elements. In at least one embodiment, a moving element can be modified using subsequent instances of an operating characteristic. In at least one embodiment, a baseline or model itself can be modified using subsequent instances of an operating characteristic.

[0037] It will be appreciated that, after reading this disclosure, a fault or impending fault is not necessarily indicated by one or more operating characteristics that deviate from normal operating characteristics, a baseline, or a model. Thus, in at least one embodiment, there may be a range of abnormalities but not necessarily operating characteristics of the fault. As described above, a fault as used herein includes a failure to function adequately, or to perform satisfactorily, such as an inability to actuate or open quickly enough, a failure to pass enough media, excessive leakage, etc. It will therefore be appreciated that a fault as used herein is beyond normal or abnormal behavior. While abnormal behavior does not require immediate repair, replacement, or maintenance, a fault is more urgent and may actually stop the system 100, or require stopping the system 100. It will then be appreciated that, after reading this disclosure, a fault should be avoided where possible.

[0038] Thus, methods according to the present disclosure attempt to predict failures before they occur. For example, once module 200 has learned the operating characteristics of system 100 and / or components 102 of system 100, such as when operating normally, module 200 does not necessarily need to predict failures when those operating characteristics deviate from normal into an abnormal range, as reactive alarm systems of the prior art might. Rather, in at least one embodiment, module 200 generates a failure prediction when recent trends in the operating characteristics of one or more components 102 of system 100 indicate that any one or another of those components 102 may fail. For example, module 200 may notice that pressures or flow rates associated with multiple components 102 are decreasing, such as when the pressure or flow rate associated with multiple components 102 is decreasing. Figure 5 As shown, a fault may be predicted—not necessarily of those components 102—but perhaps of another component, such as a compressor supplying those components 102. As another example, a decreased pressure or flow rate associated with one component 102a and an increased pressure or flow rate associated with other components 102b, 102c (such as Figure 4 ) connection may indicate an impending failure of the initial component 102a.

[0039] In at least one embodiment, predicting component failures may include identifying patterns in trends of operating characteristics. In at least one embodiment, the patterns may be based on previous component failures. In at least one embodiment, the patterns may be based on multiple components 102. In at least one embodiment, the patterns may be based on interactions between components 102. In at least one embodiment, the patterns may be based on components 102 other than the component 102 whose failure is predicted.

[0040] In at least one embodiment, the previous component failure may be or be related to the failure of the component 102 that is predicted to fail. For example, in the case where the component 102 has failed, been repaired and put back into service, the module 200 may use its behavior before the previous failure to predict subsequent failures. In at least one embodiment, the previous component failure may be or be related to a different component that previously performed the function of the component that was predicted to fail. For example, in the case where the component 102 fails and is replaced, the module 200 may use the behavior of the previous component before its failure to predict the subsequent failure of the new component 102 that performs the safety function. In at least one embodiment, the previous component failure may be or be related to a different component other than the component that is predicted to fail. For example, as described above, the module 200 may predict a compressor leak or other failure based on a low downstream pressure or flow rate associated with one or more other components 102, even if the pressure or flow rate associated with the compressor itself does not indicate a failure.

[0041] In at least one embodiment, when predicting component failure, a trend may be associated with one or more components, while a baseline may be associated with multiple components. In at least one embodiment, a trend may be a more recent trend when compared to an instance of an operating characteristic used to create a baseline or model. In at least one embodiment, a trend may include an instance of an operating characteristic used to create a baseline or model.

[0042] In at least one embodiment, the module 200 can predict when a component 102 (such as a pneumatic cylinder, a pneumatically controlled valve, etc.) will fail based on a trend of increased cycle time of the component 102. In at least one embodiment, the module 200 can predict when a component 102 will fail based on a trend of decreased flow rate through the component 102. In at least one embodiment, the module 200 can predict when a component 102 will fail based on a trend of increased flow rate to and / or through the component 102. In at least one embodiment, the module 200 can predict when a component 102 will fail based on a trend of decreased pressure associated with the component 102.

[0043] In at least one embodiment, the module 200 may predict when a component 102 (such as a pneumatic cylinder, a pneumatically controlled valve, etc.) will fail based on trends associated with other components 102. In at least one embodiment, the module 200 may predict when a component 102 will fail based on trends of increased cycle time for one component 102 compared to another component 102. In at least one embodiment, the module 200 may predict when a component 102 will fail based on trends of decreased flow rate through one component 102 compared to another component 102. In at least one embodiment, the module 200 may predict when a component 102 will fail based on trends of increased flow rate to one component 102 compared to another component 102. In at least one embodiment, the module 200 may predict when a component 102 will fail based on trends of decreased pressure associated with one component 102 compared to another component 102.

[0044] In at least one embodiment, a method for monitoring an industrial system 100 (such as a pneumatic control system) according to the present disclosure may include monitoring a plurality of operating characteristics of a plurality of components 102 of the system 100, recording a plurality of instances of the operating characteristics of each of the components 102, creating a mathematical model of the system 100 based on the plurality of instances of the operating characteristics, predicting a failure of one of the components 102 by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include a cycle time, flow rate, and / or pressure associated with each of the one or more components 102. In at least one embodiment, the model may model the system 100 as a whole and / or may include discrete modeling of any of the components 102.

[0045] In at least one embodiment, a method for monitoring an industrial system 100 (such as a pneumatic control system) according to the present disclosure may include monitoring a plurality of operating characteristics of a plurality of components 102 of the system 100, recording a plurality of instances of the operating characteristics of each of the components 102, creating a mathematical model of the system 100 based on the plurality of instances of the operating characteristics, predicting a remaining useful life of one of the components 102 (e.g., a pneumatic cylinder) by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components 102. In at least one embodiment, the model may model the system 100 as a whole and / or may include discrete modeling of any of the components 102.

[0046] It will also be understood, after reading this disclosure, that two assemblies 102 of the same type and / or size may have different operating characteristics. For example, two brand new pneumatic cylinders of the same type and size produced in the same facility may have different stroke lengths, different actuation / opening speeds, different deactivation / closing speeds, different cycle times, other differences in operating characteristics, or any combination thereof.

[0047] In at least one embodiment, the module 200 can create and / or modify a baseline and / or model based on the operation or operation of one or more of the different operating characteristics of each component 102, including differences between individual components of the same type and size. The baseline and / or model can be compared with the future operating characteristics of each component 102 to determine or predict when each component 102 can be expected to fail and / or its remaining useful life. For example, each component 102 can have an expected useful life, which can be specified by the manufacturer. However, it is understood that a single component can fail before or after the manufacturer expects. By analyzing the baseline and / or model described herein, as well as more recent trends in the operating characteristics of each component 102, the module 200 can predict whether a single component 102 can be expected to fail before or after its specified useful life, thereby avoiding unexpected failures and maximizing the efficiency of the system 100. For example, if the module 200 detects a faster than expected degradation of the stroke length, opening speed, closing speed, cycle time, another operating characteristic, or any combination thereof, or a sudden change therein, the module 200 can predict an impending failure, thereby allowing maintenance personnel to plan replacement or repair of the component 102 before it actually fails. On the other hand, if the module 200 detects a slower than expected degradation of the stroke length, opening speed, closing speed, cycle time, another operating characteristic, or any combination thereof, the module 200 can predict an extended useful life beyond a specified useful life, thereby optimizing the efficiency of the system 100 by at least avoiding unnecessary scheduled maintenance and / or unnecessary replacement of a given component 102.

[0048] In at least one embodiment, systems and methods according to the present disclosure can optimize maintenance plans by, for example, predicting which components 102 can be expected to fail between a given scheduled maintenance and the next, thereby indicating which components 102 need to be repaired or replaced during a given scheduled maintenance and which components can wait until the next one. In at least one embodiment, systems and methods according to the present disclosure can optimize maintenance plans by, for example, changing the maintenance plan so that maintenance can be planned comfortably before a cluster of components 102 is expected to fail and / or a given scheduled maintenance task can be advanced or delayed based on the predicted failure or useful life of a component 102 or group of components 102.

[0049] In at least one embodiment, a method for monitoring an industrial system 100 (e.g., a pneumatic control system) according to the present disclosure may include monitoring a plurality of operating characteristics of a plurality of components 102 of the system 100, recording a plurality of instances of the operating characteristics of each component 102, creating a mathematical model of the system 100 based on the plurality of instances of the operating characteristics, predicting an optimal pressure of the system 100 to achieve production capacity by comparing trends in the plurality of instances of the operating characteristics with the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components 102. In at least one embodiment, the model may model the system 100 as a whole and / or include discrete modeling of any of the components 102.

[0050] It is understood that the system 100 may include a large number and / or variety of components 102. In at least one embodiment, each component 102 or different components 102 may require different pneumatic pressures to perform optimally. In at least one embodiment, the module 200 can determine the optimal pressure for each component 102 or group of components 102, thereby optimizing the efficiency of the system 100 at least by avoiding unnecessary high pneumatic pressures (e.g., supply pressures). In at least one embodiment, the module 200 can predict the optimal pressure for each component 102 as the component wears or ages by analyzing the baselines and / or models described herein and updated trends in the operating characteristics of each component 102. The optimal pressure can be transmitted to maintenance personnel or other personnel and / or automatically controlled by the controller 102 and / or the module 200.

[0051] In at least one embodiment, the systems and methods according to the present disclosure can provide real-time monitoring of various operating characteristics (such as various pressures, air flow, volume consumption, time, etc.). In at least one embodiment, the systems and methods according to the present disclosure can create a baseline / model of the industrial system 100. In at least one embodiment, the systems and methods according to the present disclosure can calculate or otherwise determine deviations from the baseline / model, which can be based on thresholds and / or baseline limits. In at least one embodiment, the systems and methods according to the present disclosure can provide alerts to maintenance personnel, such as maintenance personnel, based on those deviations and / or maintenance plans, thereby avoiding unexpected failures and / or unnecessary component replacements. In at least one embodiment, the systems and methods according to the present disclosure can analyze data using machine learning algorithms (such as k-means clustering, hierarchical clustering, principal component analysis, random forest regressors, time series prediction models, or any combination thereof), and create unique machine signatures for each of the components 102 based on their unique operating characteristics and / or patterns, and predict failures, service life, and / or optimal operating pressures.

[0052] In at least one embodiment, a method for monitoring an industrial system, such as a pneumatic control system, may include monitoring a plurality of operating characteristics of the system, recording a plurality of instances of the operating characteristics, creating a baseline based on the plurality of instances of the operating characteristics, comparing trends in the plurality of instances of the operating characteristics to the baseline, predicting component failure based on the comparison, or any combination thereof. In at least one embodiment, the baseline may be or include a mathematical model of the pneumatic control system as a whole and / or a mathematical model of each monitored component of the pneumatic control system. In at least one embodiment, the baseline may include a mean value of the operating characteristic. In at least one embodiment, the baseline may include a standard deviation of the operating characteristic. In at least one embodiment, the baseline may include one or more tolerances of the operating characteristic.

[0053] In at least one embodiment, the monitored component may include one or more pneumatic cylinders, one or more pneumatically controlled valves, another component of the system, or any combination thereof. In at least one embodiment, the operating characteristic may include opening speed, closing speed, cycle time, cycle count, flow rate, pressure, torque, another operating characteristic associated with a pneumatically controlled component, or any combination thereof.

[0054] The baseline or model may be fixed and / or change over time. In at least one embodiment, the baseline may be fixed once created. In at least one embodiment, the baseline includes fixed elements and moving elements. In at least one embodiment, the moving elements may be modified using subsequent instances of operating characteristics. In at least one embodiment, the baseline itself may be modified using subsequent instances of operating characteristics.

[0055] In at least one embodiment, predicting a component failure may include identifying a pattern in a trend (such as a pattern based on previous component failures). In at least one embodiment, the previous component failure may be a failure of or related to a component whose failure is predicted. In at least one embodiment, the previous component failure may be of or related to a different component other than the component whose failure is predicted. In at least one embodiment, the previous component failure may be of or related to a different component that previously performed the function of the component whose failure is predicted. In at least one embodiment, when predicting a component failure, a trend may be associated with one or more components, while a baseline may be associated with multiple components. In at least one embodiment, a trend may be a more recent trend when compared to an instance of an operating characteristic used to create a baseline or model. In at least one embodiment, a trend may include an instance of an operating characteristic used to create a baseline or model.

[0056] In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased cycle time of the pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased flow rate through the pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased flow rate to and / or through the pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased pressure associated with the pneumatically controlled component.

[0057] In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased cycle time of one pneumatically controlled component compared to another pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased flow rate through one pneumatically controlled component compared to another pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of increased flow rate to one pneumatically controlled component compared to another pneumatically controlled component. In at least one embodiment, predicting component failure may include predicting when a pneumatically controlled component will fail based on a trend of decreased pressure associated with one pneumatically controlled component compared to another pneumatically controlled component.

[0058] In at least one embodiment, the system may include one or more pneumatic cylinders configured to linearly move one or more loads, one or more PLCs, one or more gateway computers, one or more regulators, one or more sensors, or any combination thereof. In at least one embodiment, the system may be configured to receive real-time values ​​from one or more components and / or sensors, train an artificial intelligence engine based on the real-time values, prepare a machine learning model, detect unique data patterns or machine signatures of one or more components, predict pressure consumption for a number of operating cycles, calculate the remaining useful life of one or more components, or any combination thereof. In at least one embodiment, the system may optimize the performance and / or maintenance of one or more pneumatic cylinders. In at least one embodiment, the system may fine-tune the settings of key parameters such as pressure to obtain better performance and cost savings. In at least one embodiment, the system may predict the amount of time remaining before a pneumatic cylinder reaches the end of its operational life and / or generate a plan to replace one or more pneumatic cylinders before a potential failure.

[0059] In at least one embodiment, a method for monitoring an industrial system, such as a pneumatic control system, may include monitoring a plurality of operating characteristics of a plurality of components of the system, recording a plurality of instances of the operating characteristics of each of the components, creating a mathematical model of the system based on the plurality of instances of the operating characteristics, predicting failure of one of the components by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components, or any combination thereof. In at least one embodiment, the model may model the system as a whole and / or include discrete modeling of any of the components.

[0060] In at least one embodiment, a method for monitoring an industrial system, such as a pneumatic control system, may include monitoring a plurality of operating characteristics of a plurality of components of the system, recording a plurality of instances of the operating characteristics of each of the components, creating a mathematical model of the system based on the plurality of instances of the operating characteristics, predicting the remaining useful life of one of the components by comparing trends in the plurality of instances of the operating characteristics to the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components, or any combination thereof. In at least one embodiment, the method may include calculating the remaining useful life of a pneumatic cylinder. In at least one embodiment, the model may model the system as a whole and / or include discrete modeling of any of the components.

[0061] In at least one embodiment, a method for monitoring an industrial system (e.g., a pneumatic control system) may include monitoring multiple operating characteristics of multiple components of the system, recording multiple instances of the operating characteristics of each of the components, creating a mathematical model of the system based on multiple instances of the operating characteristics, predicting the optimal pressure of the system to achieve production capacity by comparing trends in multiple instances of the operating characteristics with the model, or any combination thereof. In at least one embodiment, the operating characteristics may include cycle time, flow rate, and pressure associated with each of the components, or any combination thereof. In at least one embodiment, the model may model the system as a whole and / or include discrete modeling of any of the components. In at least one embodiment, a non-transitory computer-readable medium may have instructions stored thereon that, when executed by a processor, cause the processor to perform a method as shown and / or described herein.

[0062] Without departing from the spirit of the applicant's disclosure, other and further embodiments utilizing one or more aspects of the present disclosure may be designed. For example, devices, systems and methods may be implemented for many different types and sizes in many different industries. In addition, various methods and embodiments of devices, systems and methods may be included in combination with each other to produce variations of the disclosed methods and embodiments. Discussions of singular elements may include plural elements, and vice versa. Unless otherwise specifically limited, the order of steps may occur in various orders. The various steps described herein may be combined with other steps, interlaced with the steps, and / or divided into multiple steps. Similarly, elements have been functionally described, and elements may be embodied as separate components or may be combined into components with multiple functions.

[0063] The present invention has been described in the context of preferred embodiments and other embodiments, and not every embodiment of the present invention has been described. Obvious modifications and alterations to the described embodiments may be made by those of ordinary skill in the art having the benefit of this disclosure. The disclosed and undisclosed embodiments are not intended to limit or restrict the scope or applicability of the invention conceived by the applicant, but in accordance with patent law, the applicant intends to fully protect all such modifications and improvements that fall within the scope or range of equivalents of the appended claims.

Claims

1. A method comprising: monitors multiple operating characteristics of pneumatic control systems; recording a plurality of instances of the operational characteristic; creating a baseline based on the plurality of instances of the operational characteristic; comparing trends in the plurality of instances of the operating characteristic to the baseline; as well as Component failure is predicted based on the comparison. 2 . The method of claim 1 , wherein monitoring the plurality of operating characteristics comprises monitoring cycle time, flow rate, and pressure associated with a pneumatically controlled component. 3 . The method of claim 1 , wherein monitoring the plurality of operating characteristics comprises monitoring opening speed, closing speed, cycle count, flow rate, pressure, and torque associated with a pneumatically controlled component. The method of claim 1 , wherein the baseline comprises a mathematical model of the pneumatic control system. 5 . The method of claim 1 , wherein the baseline comprises a mathematical model of each monitored component of the pneumatic control system. The method of claim 1 , wherein the baseline is fixed once created.

7. The method of claim 1, wherein the baseline comprises fixed elements and moving elements, and wherein, The mobile element is modified using subsequent instances of the operating characteristic. The method of claim 1 , wherein the baseline is modified using subsequent instances of the operating characteristic.

9. The method of claim 1, wherein predicting the component failure comprises identifying patterns in the trends.

10. The method of claim 9, wherein the pattern is based on previous component failures.

11. The method of claim 1 , wherein predicting the component failure comprises comparing a trend associated with a component to the baseline, the baseline being associated with a plurality of components. 12 . The method of claim 1 , wherein predicting the component failure comprises predicting when the pneumatically controlled component will fail based on a trend of increasing cycle time of the pneumatically controlled component.

13. The method of claim 1, wherein predicting the component failure comprises predicting when the pneumatically controlled component will fail based on a trend of a reduced flow rate through the pneumatically controlled component.

14. The method of claim 1, wherein predicting the component failure comprises predicting when the pneumatically controlled component will fail based on a trend of an increased flow rate to the pneumatically controlled component.

15. The method of claim 1, wherein predicting the component failure comprises predicting when the pneumatically controlled component will fail based on a trend of reduced pressure associated with the pneumatically controlled component.

16. The method of claim 1, wherein predicting the component failure comprises predicting when a first pneumatically-controlled component will fail based on a trend of increased cycle time of the first pneumatically-controlled component compared to a second pneumatically-controlled component.

17. The method of claim 1, wherein predicting the component failure comprises predicting when a first pneumatically-controlled component will fail based on a trend of reduced flow rate through the first pneumatically-controlled component compared to a second pneumatically-controlled component.

18. The method of claim 1, wherein predicting the component failure comprises predicting when a first pneumatically-controlled component will fail based on a trend of increased flow rate to the first pneumatically-controlled component compared to a second pneumatically-controlled component.

19. The method of claim 1, wherein predicting the component failure comprises predicting when a first pneumatically-controlled component will fail based on a trend of reduced pressure associated with the first pneumatically-controlled component compared to a second pneumatically-controlled component.

20. A method comprising: monitoring a plurality of operating characteristics of a plurality of components of a pneumatic control system, wherein the operating characteristics include a cycle time, a flow rate, and a pressure associated with each of the plurality of components; recording a plurality of instances of the operational characteristic of each of the plurality of components; creating a mathematical model of the pneumatic control system based on the plurality of instances of the operating characteristic, wherein the model includes modeling each of the plurality of components; as well as At least one of a failure of one of the plurality of components, a remaining useful life of one of the plurality of components, an optimal pressure of the pneumatic control system, and combinations thereof is predicted by comparing trends in the plurality of instances of the operating characteristic to the model.

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