Device and method for monitoring dynamic behavior changes of a process and non-transitory computer readable medium

Abstract

The present disclosure provides a method, the method comprising collecting (601) process data collected in an industrial process control and automation system (100). The method also comprises reducing (603) a dimensional space of the process data by combining two or more parameters (405, 410, 415) of the process data or examining a frequency response of the process data. The method also comprises determining (605) a change in the process based on a change in the process data in the reduced dimensional space. The method also comprises outputting (607) a result based on the determined change in the process.

Description

Technical Field

[0001] The present disclosure generally relates to industrial process control and automation systems. More specifically, the present disclosure relates to systems and methods for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space. Background Art

[0002] Industrial process control and automation systems are often used to automate large and complex industrial processes. These types of systems typically include sensors, actuators, and controllers. Some of the controllers typically receive measurements from the sensors and generate control signals for the actuators. Other controllers typically perform higher-level functions such as planning, scheduling, and optimizing operations.

[0003] The quality of the products produced in industrial processes often depends greatly on having an automatic control system for the process to operate well. Most automatic control loops or multivariable controllers have tuning parameters that are adapted to the dynamic behavior of the process. When these tuning parameters are adjusted correctly, the controller takes good action, the process is well controlled and produces high-quality products. However, if the dynamic behavior of the process changes, the controller may need to be retuned to avoid degradation of control performance and the resulting poor quality production. Summary of the invention

[0004] The present disclosure provides systems and methods for monitoring changes in the dynamic behavior of a process by mapping parameters to a lower dimensional space.

[0005] In a first embodiment, a method includes acquiring process data collected in an industrial process control and automation system. The method also includes reducing a dimensional space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data. The method also includes determining a change in a process based on a change in the process data in the reduced dimensional space. The method also includes outputting a result based on the determined change in the process.

[0006] In a second embodiment, an apparatus includes at least one processing device configured to acquire process data collected in an industrial process control and automation system; reduce a dimensional space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data; determine a change in a process based on a change in the process data in the reduced dimensional space; and output a result based on the determined change in the process.

[0007] In a third embodiment, a non-transitory computer-readable medium includes instructions that, when executed, cause at least one processing device to acquire process data collected in an industrial process control and automation system; reduce a dimensional space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data; determine a change in a process based on a change in the process data in the reduced dimensional space; and output a result based on the determined change in the process.

[0008] Other technical features will be apparent to those skilled in the art from the following drawings, descriptions and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] For a more complete understanding of the present disclosure, reference is now made to the following description taken in conjunction with the accompanying drawings, in which:

[0010] Figure 1 An exemplary industrial process control and automation system according to the present disclosure is shown;

[0011] Figure 2 shows a graph showing an example of conventional process data for a single-input single-output system according to the present disclosure;

[0012] Figure 3 shows an exemplary Bode plot according to the present disclosure;

[0013] Figure 4 shows a graph showing first order plus pure lag process model parameter estimates periodically over a period of time during a process in accordance with the present disclosure;

[0014] Figure 5 An exemplary apparatus supporting a method for monitoring changes in dynamic behavior of a process by mapping parameters to a lower dimensional space according to the present disclosure is shown; and

[0015] Figure 6 An exemplary method for monitoring changes in dynamic behavior of a process by mapping parameters to a lower dimensional space according to the present disclosure is shown. DETAILED DESCRIPTION

[0016] The accompanying drawings discussed below and the various embodiments for describing the principles of the present invention in this patent document are only carried out by way of illustration and should not be construed as limiting the scope of the present invention in any way. Those skilled in the art will appreciate that the principles of the present invention can be implemented in any type of suitably arranged device or system.

[0017] Figure 1 An exemplary industrial process control and automation system 100 according to the present disclosure is shown. Figure 1As shown, system 100 includes various components that facilitate the production or processing of at least one product or other material. For example, system 100 can be used to facilitate the control of components in one or more industrial plants. Each plant represents one or more processing facilities (or one or more parts thereof), such as one or more manufacturing facilities for producing at least one product or other material. In general, each plant can implement one or more industrial processes and can be referred to as a process system individually or collectively. A process system generally represents any system or part thereof that is configured to process one or more products or other materials in some manner.

[0018] exist Figure 1 , system 100 includes one or more sensors 102a and one or more actuators 102b. Sensors 102a and actuators 102b represent components in a process system that can perform any of a variety of functions. For example, sensor 102a can measure a variety of characteristics in a process system, such as pressure, temperature, or flow rate. In addition, actuator 102b can change a variety of characteristics in a process system. Each sensor in sensor 102a includes any suitable structure for measuring one or more characteristics in a process system. Each actuator in actuator 102b includes any suitable structure for operating or affecting one or more conditions in a process system.

[0019] At least one network 104 is coupled to the sensor 102a and the actuator 102b. The network 104 facilitates interaction with the sensor 102a and the actuator 102b. For example, the network 104 can transmit measurement data from the sensor 102a and provide control signals to the actuator 102b. The network 104 can represent any suitable network or combination of networks. As a specific example, the network 104 can represent at least one Ethernet network, an electrical signal network (such as a HART network), a pneumatic control signal network, or any other or additional type of network.

[0020] The system 100 also includes various controllers 106. The controllers 106 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first group of controllers 106 can use measurements from one or more sensors 102a to control the operation of one or more actuators 102b. A second group of controllers 106 can be used to optimize the control logic or other operations performed by the first group of controllers. A third group of controllers 106 can be used to perform additional functions. The controllers 106 can communicate via one or more networks 108 and associated switches, firewalls, and other components.

[0021] Each controller 106 includes any suitable structure for controlling one or more aspects of an industrial process. For example, at least some of the controllers 106 may represent proportional integral derivative (PID) controllers or multivariable controllers, such as controllers implementing model predictive control or other advanced predictive control. As a specific example, each controller 106 may represent a computing device running a real-time operating system, a WINDOWS operating system, or other operating system.

[0022] Operator access and interaction with the controller 106 and other components of the system 100 may occur via various operator consoles 110. Each operator console 110 may be used to provide information to the operator and receive information from the operator. For example, each operator console 110 may provide information identifying the current state of the industrial process to the operator, such as the values ​​of various process variables associated with the industrial process and alarms. Each operator console 110 may also receive information that affects how the industrial process is controlled, such as by receiving set values ​​or control modes for process variables controlled by the controller 106 or receiving other information that changes or affects how the controller 106 controls the industrial process. Each operator console 110 includes any suitable structure for displaying information to the operator and interacting with the operator. For example, each operator console 110 may represent a computing device running a WINDOWS operating system or other operating system.

[0023] Multiple operator consoles 110 may be grouped together and used in one or more control rooms 112. Each control room 112 may include any number of operator consoles 110 arranged in any suitable arrangement. In some embodiments, multiple control rooms 112 may be used to control an industrial plant, such as when each control room 112 contains an operator console 110 for managing a discrete portion of the industrial plant.

[0024] The control and automation system 100 also includes at least one historical database 114 and one or more servers 116. The historical database 114 represents a component that stores various information about the system 100. The historical database 114 may, for example, store information generated by the various controllers 106 during the control of one or more industrial processes. The historical database 114 includes any suitable structure for storing information and facilitating retrieval of information. Although shown here as a single component, the historical database 114 may be located elsewhere in the system 100, or multiple historical databases may be distributed at different locations in the system 100.

[0025] Each server 116 represents a computing device that executes an application for a user of the operator console 110 or other applications. The application may be used to support various functions for the operator console 110, the controller 106, or other components of the system 100. Each server 116 may represent a computing device running a WINDOWS operating system or other operating system. It is noted that while shown as being located locally within the control and automation system 100, the functionality of the server 116, the historian 114, or both may be remote from the control and automation system 100. For example, the functionality of the server 116 and / or the historian 114 may be implemented in a computing cloud 118 or a remote server that is communicatively coupled to the control and automation system 100 via a gateway 120.

[0026] Generally speaking, the quality of the products produced in industrial processes often depends largely on whether there is an automatic control system for the process to operate well. Most automatic control loops or multivariable controllers have tuning parameters that are adapted to the dynamic behavior of the process. When these tuning parameters are adjusted correctly, the controller takes good action, the process is well controlled and produces high-quality products. However, if the dynamic behavior of the process changes, the controller may need to be retuned to avoid degradation of control performance and the resulting poor quality production.

[0027] The behavior and operation of an industrial process control and automation system (such as system 100) can be modeled using one or more process models. An accurate process model may be beneficial or necessary for many reasons. For example, a process model may be used to make predictions about a process or for tuning a controller. Determining when a process behavior changes significantly is often a difficult problem. For many industrial processes, process model parameters are estimated from data, and the data used in the estimation often comes from planned experiments designed to create information-rich data. If the process changes over time, the process model may need to be re-identified. However, planned experiments may be time consuming and may have product quality risks. Therefore, there is a strong motivation to be able to determine when a process behavior changes so that experiments can be performed only when necessary.

[0028] Some process behavior monitoring techniques involve periodically estimating process model parameters based on regular process data. These parameter estimates are monitored over time, and when significant deviations from nominal values ​​are detected, it is concluded that a change in process behavior has occurred. However, when the process data is noisy, the model parameter estimates may also be noisy. When there are multiple parameters, the variability of the estimates can make it difficult to determine when a significant shift in value has occurred. Generally speaking, the more parameters there are, the more difficult it is to detect a change.

[0029] To address these and other problems, embodiments of the present disclosure provide systems and methods for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space. Embodiments disclosed herein take into account that when correlations exist between parameters, dimensionality reduction can be used to make behavior excursions easier to detect. To this end, the dimensionality of the parameter space is reduced to simplify the task of detecting deviations from nominal values. Embodiments disclosed herein employ one or more techniques to determine when process behavior has changed by monitoring process model parameter estimates in a lower dimensional space.

[0030] A variety of parameter space reduction techniques can be used. In the first technique, the time delay and time constant parameters can be added together to create a resulting combined response time parameter. It has been found that this combined parameter is not as variable as the individual parameters. In the second technique, the frequency response of the process model can be examined. The gain and phase shift at key frequencies (such as the cutoff frequency of the nominal model) can be viewed. This reduces the parameter space to two parameters that can be monitored over time. Using any of these techniques, the parameter space of the model is reduced to two parameters with reduced noise. In some embodiments, Figure 1 One or more components in (such as operator console 110, historical database 114, server 116, or computing cloud 118) may be configured to perform these operations.

[0031] The disclosed embodiments of the present invention allow for fewer and more stable parameters to monitor, which makes monitoring easier and more likely to detect changes in process behavior. Once changes in process behavior are detected, human intervention or automated intervention can be triggered to conduct plant experiments, thereby generating more accurate process models for controller retuning and maintaining good process control. These benefits represent technical advantages over conventional systems where noisy data obscures changes in process behavior. Additional details about the disclosed embodiments of the present invention are provided below.

[0032] Although Figure 1 One example of an industrial process control and automation system 100 is shown, but may be used for Figure 1 Various modifications may be made. For example, system 100 may include any number of sensors, actuators, controllers, networks, operator stations, control rooms, historical databases, servers, and other components. In addition, Figure 1 The composition and arrangement of system 100 in FIG. 1 is for illustration only. Components may be added, omitted, combined, further subdivided, or placed in any other suitable configuration according to specific needs. In addition, specific functions have been described as being performed by specific components of system 100. This is for illustration only. In general, control systems and automation systems are highly configurable and may be configured in any suitable manner according to specific needs. In addition, Figure 1 An exemplary operating environment is shown in which changes in the dynamic behavior of a process can be monitored by mapping parameters to a lower dimensional space. This functionality can be used in any other suitable system.

[0033] The first technique and the second technique discussed above will now be described in more detail.

[0034] Technique 1: Use process response time to represent higher-order process dynamics.

[0035] In the first technique, the process response time can be used to represent the higher order process dynamics. The process response time can be defined as the time it takes for the process step response to reach 63.2% of its final value. For a first order plus pure lag model, the process response time is the sum of the time constant and the time delay. For a higher order model, the step response can be simulated to determine the process response time. Specifically, the higher order model can be reduced to a response time.

[0036] The process P can be modeled, for example, by a linear system of differential equations, which can be written in the form of a Laplace transfer function:

[0037] y=P1u1+P2u2+…+P m u m (1)

[0038] Where y is the process output, m is the number of process inputs, and u i for i th Process input, each P i Takes the following form:

[0039]

[0040] And b i and f i is a polynomial in the Laplace domain variable s, and d i It is a pure time delay.

[0041] The first order plus pure lag transfer function is a simple yet powerful model that can be used to adequately describe many industrial processes for the purpose of controller design. The first order plus pure lag transfer function is a specific form of equation (2) and takes the following form:

[0042]

[0043] where k i is the process gain and τ i is the process time constant.

[0044] One method to obtain a process model is to perform process model identification based on process data. Figure 2Graphs 205, 210 are shown showing examples of conventional process data for a single-input single-output system according to the present disclosure. Figure 2 As shown, graph 205 shows the process input u of an exemplary system over time. Graph 210 shows the process output y of the system over time. Another graph in graph 210 shows r over time, where r is the target output or set point.

[0045] When estimating the parameters of a process model, optimizations such as the following can be performed:

[0046]

[0047] Where y(i) is the measured process value, y θ (i) is the predicted value based on the model with parameters θ, and N is the number of data points.

[0048] The optimization finds process model parameter values ​​that minimize the sum of squared errors between measured process values ​​and values ​​predicted by the process model.Techniques for process model parameter estimation are well known and will not be described in further detail herein.

[0049] If information-rich data (e.g., data from planned plant experiments) are available, accurate estimates of process model parameters can be obtained. Typically, data from routine process operations are not information-rich, and therefore process model parameter estimates from routine operating data tend to suffer from large variances and other potential inaccuracies. However, while routine process data may not allow for accurate process model estimates, if process model parameters are re-estimated periodically, changes in process behavior may be detected as drifts in the estimates.

[0050] The goal is to monitor parameter estimates derived from routine operating data and look for changes. For example, consider the case of a paper machine with a process that produces paper. One of the most important parameters is that the paper produced has the correct basis weight. To adjust the basis weight of the paper, one method is to adjust the flow valve at the headbox. This can be represented by three parameters: gain, time delay, and time constant.

[0051] An example of a gain is if the flow valve opens by x amount, the weight of the paper changes by y amount. In a simplified example, this gain could be y / x. The time delay indicates how long it takes for any change in the weight of the paper to occur after the flow valve is adjusted. The time constant measures the "ramp time" of a gradual change; that is, the time constant indicates the length of time from when the change is first measured until the new steady-state value is measured. In the case of a paper machine, the time constant could indicate how long it takes for the concentration in the headbox to increase from the previous steady-state value to the new steady-state value after more stock is added to the headbox.

[0052] For a first order plus pure lag model, both the time constant and the time delay affect how quickly the process responds, and there may be correlations between the estimates of these parameters. For example, the time constant and the time delay tend to move in opposite directions. For this reason, it may be advantageous to simply add the two parameters together and monitor the sum, which is generally the more stable parameter. It should be noted that for a first order plus pure lag process, the sum of the time constant plus the time delay is 63.2% of the process response time. This technique can also be generalized to higher order processes. A simple approach is to simulate a step response to determine the process response time.

[0053] This can be considered a dimensionality reduction technique because using this technique, two parameters (gain and response time) can be monitored to detect changes in process behavior, rather than monitoring gain, time constant and time delay, or even more parameters depending on the order of the process model. There are many other dimensionality reduction techniques that can be used for this problem.

[0054] Technique 2: Use the phase at the model cutoff frequency to represent the process dynamics.

[0055] A second process model parameter dimensionality reduction technique for monitoring changes in process behavior is to look at the frequency response of the process model. As represented by a Bode plot, the frequency response of the process model shows the process gain and phase as a function of frequency.

[0056] The Bode plot is derived as follows. For a process model in the form of a transfer function, such as in equation (2) above, P i (jω) is obtained by substituting s = jω. The polar coordinate form of this complex-valued function is:

[0057]

[0058] Where |P i (jω)| is the amplitude ratio and is the phase angle as a function of ω. A Bode plot is simply a graph of these two functions. Bode plots and frequency domain analysis are well-established fields and further details will not be included in this article.

[0059] Figure 3An exemplary Bode plot 300 according to the present disclosure is shown. By examining the Bode plot 300, a cutoff frequency 305 and a phase 310 at the cutoff frequency can be determined. The cutoff frequency 305 is determined by finding the frequency at which the amplitude ratio is equal to 0.707, or -3 if the magnitude is expressed in decibels. The phase 310 at the cutoff frequency 305 is simply the value of the phase at the cutoff frequency. In the Bode plot 300, the cutoff frequency 305 is approximately 0.1 rad / s, and the phase 310 at this frequency is approximately -100 degrees. Regardless of the order of the polynomial that makes up the transfer function in equation (2), the model can be summarized by the Bode plot 300. The phase plot is associated with the dynamics of the model, and large changes in phase (especially changes near the cutoff frequency) can be associated with important changes in the process dynamics.

[0060] To this end, the phase at the cutoff frequency can be used to represent the dynamics of the process model (regardless of the order of the process model) in order to monitor changes in process behavior. It should be clear that if a series of models are compared, the frequency of interest can be selected as the cutoff frequency of the nominal model. The dynamics of subsequent models can then be summarized by their gain and phase at the frequency of interest (the cutoff frequency of the nominal model).

[0061] Rather than monitoring all individual parameters associated with the process dynamics, the phase at a critical frequency (such as the cutoff frequency of the nominal model) can be monitored. This is demonstrated in the example below.

[0062] Consider Figure 2 Conventional process data is shown. To monitor the process, process model parameter estimates may be made periodically (e.g., every 30 minutes). Then, each time 30 minutes have passed, the process model parameters may be estimated using a recent window of data (e.g., the last four hours). The parameter estimates may then be converted to a reduced dimensional space. For example, the response time of the model may be calculated, or the phase at a cutoff frequency of a nominal model may be calculated. The reduced dimensional estimates may then be monitored over time, and a shift in the value beyond a threshold value about the nominal value indicates one or more changes in process behavior.

[0063] When it is determined that the process behavior has changed, then a stimulus (eg, one or more disturbances, perturbations, etc.) may be added to the process to generate an information-rich data set so that an accurate model of the new process behavior may be obtained.

[0064] Figure 4A graph 400 is shown showing first order plus pure lag process model parameter estimates made periodically over a period of time during a process in accordance with the present disclosure. Gains, time constants, and time delays are all estimated over a period of time (here, sixteen hours) and plotted in graphs 405, 410, and 415, respectively, along with nominal model values ​​and thresholds for change detection. In addition, the estimated process response time and phase at the nominal cutoff frequency are also plotted in graphs 420 and 425, respectively. Here, the estimated response time graph 420 is the sum of the estimated time constant graph 410 and the estimated time delay graph 415, as determined using Technique 1 above. Graph 415 showing the phase at the nominal cutoff frequency is obtained using Technique 2 above.

[0065] In the data set shown in chart 400, there is a process change starting at about 10 hours (specifically, the time constant changes to a value outside the threshold). The goal is to detect changes by examining the data in the different graphs 405-425. The estimated gain graph 405 shows that no estimated value exceeds the threshold near the nominal value at any time, which is correct. The estimated time constant graph 410 shows a significant fluctuation in the estimated value over time, where the estimated value incorrectly exceeds the detection threshold several times before the 10 hour mark (even if the actual value is within the threshold before the 10 hour mark). In addition, the estimated value from 11 hours to 13 hours is incorrectly within the threshold (even if the actual value of the time constant moves outside the threshold starting at about the 10 hour mark). Therefore, the time constant estimation data (as represented by the estimated time constant graph 410) is unreliable for monitoring change detection. There is a similar problem with the time delay estimation data.

[0066] However, the estimated response time graph 420 more clearly indicates the change that occurs at approximately 10 hours. The estimated response time graph 420 shows that the estimated value is within the threshold until approximately the 10 hour mark. After the 10 hour mark, the graph 420 shows that the estimated value is outside the threshold. This clear pattern is required for reliable change detection. Curve 425 indicates the phase at the nominal cutoff frequency, which shows that the estimated value moves outside the threshold only a few times before the 10 hour mark. After the 10 hour mark, the estimated value is outside the threshold, except for the estimated value at 13.5 hours. Therefore, in this data set, Technique 2 (using the phase at the nominal cutoff frequency) is not as reliable as Technique 1 (process response time) in detecting changes; however, Technique 2 is still better than monitoring the time constant and time delay alone.

[0067] Figure 4The example shown demonstrates that reducing the number of parameters to be monitored is helpful when monitoring changes in process behavior by examining process model parameter estimates over time. In many cases, reducing the dimensionality of the parameter space can improve the reliability of monitoring. The techniques described herein provide different methods for summarizing parameters associated with process dynamics (other than gain) by process response time or by phase at a cutoff frequency.

[0068] Figure 5 An exemplary device 500 is shown that supports a method for monitoring changes in the dynamic behavior of a process by mapping parameters to a lower dimensional space according to the present disclosure. The device 500 may, for example, represent Figure 1 The device 500 may be implemented in an operator console 110, a historical database 114, a server 116, or a device in a computing cloud 118. However, any other suitable device or system may be used to implement these components, and the device 500 may be used in any other suitable system.

[0069] like Figure 5 As shown in FIG. 5 , device 500 includes at least one processor 502, at least one storage device 504, at least one communication unit 506, and at least one input / output (I / O) unit 508. Each processor 502 can execute instructions, such as those that implement the above-described processes and methods, which can be loaded into memory 512. Each processor 502 represents any suitable processing device, such as one or more microprocessors, microcontrollers, digital signal processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or discrete circuits.

[0070] Memory 512 and persistent storage 514 are examples of storage devices 504, which represent any structure capable of storing information (such as data, program code, and / or other suitable information, whether temporary or permanent) and facilitating retrieval of the information. Memory 512 may represent random access memory or any other suitable volatile or non-volatile storage device. Persistent storage 514 may include one or more components or devices that support long-term storage of data, such as read-only memory, a hard drive, flash memory, or an optical disk. Memory 512 or persistent storage 514 may be configured to store information and data associated with monitoring changes in the dynamic behavior of a process by mapping parameters to a lower dimensional space.

[0071] The communication unit 506 supports communications with other systems or devices. For example, the communication unit 506 may include a network interface card or a wireless transceiver to facilitate communication over a wired network or a wireless network, such as the network 108. The communication unit 506 may support communication over any suitable physical or wireless communication link.

[0072] I / O unit 508 allows for input and output of data. For example, I / O unit 508 can provide a connection for user input via a keyboard, mouse, keypad, touch screen, or other suitable input device. I / O unit 508 can also send output to a display, printer, or other suitable output device.

[0073] Although Figure 5 One example of an apparatus 500 supporting a method for monitoring changes in the dynamic behavior of a process by mapping parameters to a lower dimensional space is shown, but may be used for Figure 5 Make various changes. For example, you can combine, further subdivide, or omit Figure 5 In addition, computing devices can be configured in many ways, and Figure 5 The present disclosure is not limited to any particular configuration of computing devices.

[0074] Figure 6 An exemplary method 600 for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space according to the present disclosure is shown. The method 600 may be used, for example, to monitor industrial process control and automation systems such as Figure 1 In some embodiments, the method 600 may be performed by one or more components of the system 100, such as the operator station 110, the server 116, or the computing cloud 118. However, the method 600 may be used with any other suitable system. For ease of explanation, the method 600 will be described as being performed by a computing device such as Figure 5 The device 500 is executed.

[0075] At step 601, a device collects process data collected during execution of a process in an industrial process control and automation system. This may include, for example, the device collecting process data from a historical database. The process data may be output from one or more controllers in the industrial process control and automation system. In some embodiments, after the process data is output from the one or more controllers, the process data is stored in a historical database. In some embodiments, the data is collected in real time to a certain extent, because batches of data will be uploaded to the device every minute or every few seconds.

[0076] At step 603, the device reduces the dimensional space of the process data by combining two or more parameters of the process data or examining the frequency response of the process data. In some embodiments, the device reduces the dimensional space of the process data by combining a time constant parameter and a time delay parameter into a response time parameter. In some embodiments, the device reduces the dimensional space of the process data into a gain and a phase shift of the process data at a cutoff frequency.

[0077] At step 605, the device determines a change in the process based on the change in the process data in the reduced dimensional space. In some embodiments, the device determines the change in the process by inputting the process data in the reduced dimensional space into an algorithm and receiving an output of the algorithm indicating that the process data has exceeded a predetermined threshold.

[0078] At step 607, the device outputs a result based on the determined change in the process. In some embodiments, the output result includes an alarm or warning issued to a user, wherein the alarm or warning instructs the user to re-tune or calibrate the component of the industrial process control and automation system. In some embodiments, the output result includes a signal or instruction transmitted by the device to the component of the industrial process control and automation system, wherein the signal or instruction is configured to automatically change the setting of the component.

[0079] Although Figure 6 One example of a method 600 for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space is shown, but may be used for Figure 6 Various changes may be made. For example, although shown as a series of steps, Figure 6 The various steps shown in the embodiment can overlap, occur in parallel, occur in different orders, or occur multiple times. In addition, some steps can be combined or removed, and additional steps can be added according to specific needs.

[0080] In some embodiments, the various functions described in this patent document are implemented or supported by a computer program, which is formed by a computer-readable program code and is embodied in a computer-readable medium. The phrase "computer-readable program code" includes any type of computer code, including source code, object code and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as a read-only memory (ROM), a random access memory (RAM), a hard drive, a compact disc (CD), a digital video disc (DVD) or any other type of memory. "Non-transient" computer-readable medium excludes wired, wireless, optical or other communication links that transmit instantaneous electrical signals or other signals. Non-transient computer-readable medium includes a medium that can permanently store data and a medium that can store and rewrite data afterwards, such as a rewritable optical disc or an erasable memory device.

[0081] It may be advantageous to set forth the definitions of certain words and phrases used throughout this patent document. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, processes, functions, objects, classes, instances, related data or a portion thereof that are suitable for implementation with suitable computer code (including source code, object code or executable code). The term "communication" and its derivatives cover both direct communication and indirect communication. The terms "include" and "comprise" and their derivatives mean including but not limited to this. The term "or" is inclusive, meaning and / or. The phrase "associated with..." and its derivatives may mean including, included within, interconnected with, included within, connected to or connected with, coupled to or coupled with, communicable with, collaborate with, interlace, juxtapose, close to, coupled to or coupled with, have, have the property of, have a relationship with, or have a relationship with. When used with a list of items, the phrase "at least one of..." means that different combinations of one or more of the listed items may be used, and only one item in the list may be required. For example, “at least one of A, B, and C” includes any combination of A, B, C, A and B, A and C, B and C, and A, B, and C.

[0082] The description in this application should not be construed as implying that any particular element, step, or function is an essential or critical element that must be included within the scope of the claims. The scope of subject matter protected by a patent is limited only by the claims as allowed. In addition, none of the claims is intended to invoke 35 U.S.C. §112(f) with respect to any of the appended claims or claim elements, unless the exact words "means for..." or "step for..." followed by a participle phrase identifying the function are expressly used in a particular claim. The use of terms such as (but not limited to) "mechanism", "module", "device", "unit", "component", "element", "member", "device", "machine", "system", "processor" or "controller" within the claims is understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and is not intended to invoke 35 U.S.C. §112(f).

[0083] Although the present disclosure has described certain embodiments and generally associated methods, changes and permutations of these embodiments and methods will be apparent to those skilled in the art. Therefore, the above description of the exemplary embodiments does not limit or restrict the present disclosure. Other changes, substitutions and variations are also possible without departing from the spirit and scope of the present disclosure as defined in the following claims.

Claims

1. A method for monitoring changes in dynamic behavior of a process, the method comprising: collecting (601) process data collected in an industrial process control and automation system (100); reducing (603) the dimensional space of the process data by combining two or more parameters (405, 410, 415) of the process data or examining the frequency response of the process data, wherein the dimensional space of the process data is reduced by combining a time constant parameter (410) and a time delay parameter (415) into a response time parameter (420); determining (605) a change in a process based on a change in the process data in the reduced dimensional space; and Results are output (607) based on the determined changes to the process. 2 . The method of claim 1 , wherein the dimensional space of the process data is reduced by reducing a high-order model to response time parameters.

3. The method of claim 1, wherein the dimensional space of the process data is reduced to a gain (405) and a phase shift of the process data at a cutoff frequency (305, 310).

4. The method of claim 1, wherein the process data is collected from one or more controllers (106) in the industrial process control and automation system. 5 . The method of claim 1 , wherein a change in the process is determined by inputting the process data in a reduced dimensional space into an algorithm and receiving an output of the algorithm indicating that the process data has exceeded a predetermined threshold.

6. The method of claim 1, wherein the outputted results include an alert or warning issued to a user, the alert or warning instructing the user to re-tune or calibrate components of the industrial process control and automation system.

7. The method of claim 1, wherein the outputted result comprises a signal or instruction transmitted to a component of the industrial process control and automation system, wherein the signal or instruction is configured to automatically change a setting of the component.

8. A device for monitoring dynamic behavior changes of a process, the device comprising: At least one processing device (502), the at least one processing device being configured to: Acquiring process data collected in an industrial process control and automation system (100); By combining two or more parameters (405, 410, 415) of the process data The dimensional space of the process data is reduced by combining or examining the frequency response of the process data, wherein the time constant parameter (410) and the time delay parameter (415) are combining into response time parameters (420) to reduce the dimensional space of the process data; determining a change in the process based on a change in the process data in the reduced dimensional space; as well as A result is output based on the determined change in the process.

9. A non-transitory computer-readable medium (504, 512, 514) comprising instructions that, when executed, cause at least one processing device (502): Acquiring process data collected in an industrial process control and automation system (100); reducing a dimensional space of the process data by combining two or more parameters (405, 410, 415) of the process data or examining a frequency response of the process data, wherein the dimensional space of the process data is reduced by combining a time constant parameter (410) and a time delay parameter (415) into a response time parameter (420); determining a change in the process based on a change in the process data in the reduced dimensional space; and A result is output based on the determined change in the process.

Images