PID (Proportion Integration Differentiation) loop performance rating and diagnosing method based on fluctuation variable coefficient

Abstract

The invention provides a PID (Proportion Integration Differentiation) loop performance rating and diagnosing method based on a fluctuation variable coefficient, which is used for solving the problems that the control performance of a PID control loop is reduced and parameters need to be reset in industrial production. According to the method, data of a process variable (PV), a set value (SV), controller output (MV) and a mode point (MODE) are collected through a DCS, and after data preprocessing is carried out, core performance indexes such as a fluctuation variable coefficient (CV), a relative performance index (RPI) and an autocorrelation coefficient are calculated. The fluctuation variable coefficient CV is used as a dimensionless quantity, the data volatility can be effectively quantified, and the limitation of a traditional evaluation method in a multi-dimensional scene is overcome. The system grades the loops according to the indexes, the loops are divided into excellent loops, good loops, general loops and poor loops, and fault diagnosis and optimization suggestions are provided for the general loops or the poor loops. The method further comprises the steps of establishing a loop rating history library and tracking an optimization effect. The method improves the real-time performance evaluation precision and diagnosis efficiency of the PID loop, and is suitable for the field of industrial automation control.

Description

Technical Field

[0001] The invention relates to the technical field of industrial production control, and in particular to a PID loop performance rating and diagnosis method based on a fluctuation variation coefficient. Background Art

[0002] According to statistics, more than 95% of the existing automatic control loops use PID control strategies. The controllers often have good control performance in the initial operation stage. With the fluctuation of working conditions and changes in process conditions, the PID parameters of the controllers gradually fail to adapt to the new working conditions. If the new parameters cannot be adjusted in time, the control performance of the loop will gradually deteriorate, affecting production efficiency.

[0003] At present, most factories use the self-control rate of the control loop as the standard to judge the control performance of a loop, and some use Harris index and minimum variance method for evaluation. The former can only evaluate the current self-control level of the workshop, but cannot judge whether the control performance of a single loop is good or bad; the latter performs multi-angle evaluation through performance index calculation, but because the algorithm has certain defects, it cannot compare multi-dimensional situations, and needs to refer to the average value, which is easily affected when there are multiple average values.

[0004] In addition, for loops with poor control performance and waiting for parameter adjustment, it is difficult for engineers to obtain effective information and find problems in the first place. They often perform maintenance only after production benefits are affected, which easily leads to economic losses. Therefore, it is very necessary to use external means to regularly perform performance analysis, rating and diagnosis on the PID control loops in the DCS system.

[0005] Based on the above situation, it is very necessary to propose a new method for rating the PID loop control performance to address the problem of poor PID loop control performance in industrial production. Summary of the invention

[0006] This paper proposes a PID loop performance rating and diagnosis method based on the coefficient of variation, which can overcome the defects of existing methods in multi-dimensional scenarios. This method collects relevant data of the PID control loop through the DCS system, performs data preprocessing, calculates core performance indicators, and performs diagnosis based on the loop rating results, providing a basis for engineers to optimize the process.

[0007] To achieve the above objectives, the specific implementation plan is as follows:

[0008] Data collection

[0009] Based on the DCS system, the process variable (PV), set value (SV), controller output (MV) and mode point (MODE) data of the control loop to be evaluated are collected in real time to ensure the real-time and comprehensiveness of the data.

[0010] Data preprocessing

[0011] The collected data is filtered to remove bad values, and supplementary processing is performed for missing data to ensure data quality.

[0012] Performance index calculation

[0013] Based on the preprocessed data, the following three core performance indicators are calculated to rate the PID loop:

[0014] Coefficient of variation (CV)

[0015] By calculating the ratio of the standard deviation of the process variable to the mean value, the coefficient of variation CV is obtained. The calculation formula is:

[0016]

[0017] PV mean Represents the average value of PV, Err sd Represents the standard deviation of the error.

[0018] The calculation formula of the autocorrelation coefficient (RPI) is:

[0019] Introduce the PV autocorrelation coefficient, represented by ρ:

[0020]

[0021] The relative performance index can be used to evaluate the response speed of the controller relative to the benchmark (expected steady-state time). The larger the value, the faster the response speed, the smaller the value, the slower the response speed, and the value close to 1 indicates the expected response speed. In order to clearly express the oscillation degree of the control loop, the autocorrelation coefficient of PV is selected to express the correlation between the data before and after a set of data (themselves and themselves).

[0022] PV relative performance index, P r express:

[0023]

[0024] where Tr desired represents the expected steady-state time, Tr actual Indicates the actual steady-state time.

[0025] Circuit rating

[0026] Based on the above performance indicators, the control loop is rated using the loop rating table, which divides the loop into four levels: excellent, good, fair, and poor. The weight of the loop rating table is mainly concentrated on the coefficient of variation CV to ensure the accuracy of the rating.

[0027] Troubleshooting and optimization suggestions

[0028] For average or poor circuits, the system automatically generates diagnostic suggestions based on performance indicators and a diagnostic information library. The information library is built from field process experience and parameter thresholds. The system automatically matches fault codes and provides diagnostic suggestions based on rating results and measured data for engineers’ reference.

[0029] Loop Rating History Library

[0030] Establish a loop rating history library to record the historical rating information of the loop to facilitate comparison and evaluation of subsequent optimization effects.

[0031] Open loop status determination

[0032] When the self-control rate of the control loop is lower than 60%, the system defaults to judging that the loop is in an open-loop state and does not participate in the rating.

[0033] Beneficial Effects

[0034] The PID loop performance rating and diagnosis method based on the fluctuation variation coefficient proposed in the present invention has the following advantages:

[0035] The performance evaluation is performed through the dimensionless coefficient of variation CV, which solves the limitations of traditional methods in multi-dimensional situations.

[0036] Automatic diagnosis and optimization suggestions reduce engineers’ workload and maintenance costs;

[0037] Set custom performance indicator thresholds for different types of control loops to further improve rating accuracy;

[0038] The established loop rating history library provides a reference for long-term control loop optimization.

[0039] Through the above steps, the present invention effectively improves the performance evaluation accuracy and diagnostic efficiency of the PID control loop, and can be widely used in the field of industrial control to help factories improve production efficiency and reduce economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0042] Figure 2It is a schematic diagram of the process of the present invention;

[0043] Figure 3 It is a schematic diagram of some fault codes and diagnostic information of the present invention. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0045] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0046] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0047] Example 1

[0048] In order to make the implementation scheme and advantages of the present method clearer, the present invention will be further described below in conjunction with the flow chart and implementation case:

[0049] More than 95% of the loops in the existing factory DCS system are PID control loops. With the change of working conditions and occasional factors, the control parameters will gradually become incompatible and the control performance will gradually deteriorate. Engineers often fail to discover and optimize this type of problem in time, which can easily cause certain economic losses. Through real-time evaluation and rating of loop control effects, problem diagnosis can be performed on loops with poor ratings, so that problems can be solved in time and process production can be optimized.

[0050] The present invention provides a method for grading and diagnosing PID loop control performance based on a fluctuation variation coefficient. The embodiment is applied to a synthesis furnace section of a chlor-alkali process, and includes:

[0051] Filter the bit numbers (PV, SV, MV, MODE) of all required control loops and collect them into the database through the OPC server.

[0052] Set up filters to pre-process the data, remove bad values, and supplement and replace the removed bad values.

[0053] Before calculating the performance indicators, threshold rules are set for the performance indicators as a reference for rating.

[0054] Select a time interval for loop data. The timestamp is generally at least 12 hours ago. The time interval should be no less than 6 hours. The amount of data for a single bit number should be no less than 150. Otherwise, the calculation result will not be meaningful for reference.

[0055] Calculate the performance index of the loop data in the above time interval:

[0056] Among them, the error, error average and PV average of each time period are calculated according to the data sampling frequency, and the steady-state time is calculated.

[0057] The PV fluctuation coefficient of variation is introduced as a key indicator, expressed as CV:

[0058]

[0059] PV mean Represents the average value of PV, Err sd Represents the standard deviation of the error.

[0060] CV represents the amplitude ratio of the data fluctuation along the average value. The larger the value, the greater the volatility. Moreover, this kind of volatility is based on the average value, which solves the dimensionality problem. The coefficient of variation does not lose its meaning just because some data are particularly large. In short, the coefficient of variation CV value is a measure of relative fluctuation.

[0061] Introduce PV relative performance index, using P r express:

[0062]

[0063] where Tr desired represents the expected steady-state time, Tr actual Indicates the actual steady-state time.

[0064] The relative performance index is a good way to evaluate the response speed of the controller relative to the benchmark (expected steady-state time). Larger values ​​indicate faster response speeds, smaller values ​​indicate slower response speeds, and values ​​close to 1 indicate the expected response speed.

[0065] Introduce the PV autocorrelation coefficient, represented by ρ:

[0066]

[0067] In order to clearly express the oscillation degree of the control loop, the autocorrelation coefficient of PV is selected to express the correlation between a set of data before and after (themselves and themselves).

[0068] It is understandable that in addition to the three major performance indicators KPI, the over-limit conditions of the process variable PV and the controller output MV, and the change frequency of the set value SV and the mode point MODE should also be calculated to help analyze the loop situation.

[0069] It can be understood that if the process variable PV exceeds the limit for a long time, it means that the controller control effect is not good; if the controller output MV exceeds the limit for a long time and the valve position is fully open or fully closed for a long time, it means that the saturation is too high, check the valve size and control parameters; frequent changes in the set value SV indicate frequent fluctuations in the operating conditions, or check the loop control strategy to see if it is cascade control; frequent changes in the mode point MODE indicate that the loop often switches manually or automatically, check the control strategy.

[0070] Based on the performance index calculation results, taking the liquid level loop type as an example, refer to the loop rating table to rate the loop performance.

[0071]

[0072] As shown in the table above, the circuit rating is mainly composed of P r , ρ, and CV are the three main performance indicators, among which the coefficient of variation CV has the largest weight, P r The same weight as ρ.

[0073] It can be understood that the loop rating is excellent or good only when the coefficient of variation CV meets the conditions; when the coefficient of variation CV does not meet the conditions, the loop rating is fair or poor, and diagnostic optimization is required.

[0074] When only three of the performance indicators are met, the circuit is rated as excellent.

[0075] When the coefficient of variation CV meets the conditions, the relative performance index P r When either the and the autocorrelation coefficient ρ meet the conditions, the loop rating is good.

[0076] When the coefficient of variation CV does not meet the conditions, the relative performance index P r When both and the autocorrelation coefficient ρ meet the conditions, the loop rating is fair and the loop needs to be diagnosed and optimized.

[0077] When the coefficient of variation CV does not meet the conditions, the relative performance index P rWhen only one of the conditions and the autocorrelation coefficient ρ meet the requirements or both of them do not meet the requirements, the loop rating is poor and the loop needs to be diagnosed and optimized.

[0078] When the loop self-control rate is ≤60%, the loop is defaulted to open-loop state and does not participate in the rating.

[0079] Establish a fault code library and a corresponding diagnostic information library based on on-site processes, production experience and parameter thresholds. Match the corresponding fault information in the library based on the main performance indicators and bit number parameter analysis results, and display the corresponding diagnostic opinions.

[0080] The system automatically collects all loops rated as fair and poor and the corresponding diagnostic information, and displays them on the server in reports, which engineers can use to repair instruments or adjust loop control parameters.

[0081] After tuning and optimization, the control loop will be re-rated for loop control performance in the next cycle, and the new rating will be compared with the historical rating to verify the tuning and optimization results.

[0082] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0083] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A PID loop performance rating and diagnosis method based on fluctuation variation coefficient, characterized in that: The following steps are involved: S1, based on the DCS system to collect the process variable (PV), set value (SV), controller output (MV) and mode point (MODE) data of the control loop; S2, preprocessing the collected data, including removing bad values ​​and supplementing data; S3. Calculate performance indicators based on the data, including coefficient of variation (CV), relative performance index and autocorrelation coefficient; S4. Use the coefficient of variation as the core rating indicator to evaluate the control loop performance level, and combine the coefficient of variation with the relative performance index and the autocorrelation coefficient to form a loop rating table; S5. Output diagnostic suggestions based on the diagnostic information database for control loops of general and poor grades; S6. Establish a loop rating history database to record the rating changes of the loop.

2. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: The calculation formula of the coefficient of variation (CV) is: PV mean Represents the average value of PV, Err sd Represents the standard deviation of the error.

3. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: The calculation formula of the autocorrelation coefficient (RPI) is: Introduce the PV autocorrelation coefficient, represented by ρ: The relative performance index is a good way to evaluate how quickly a controller responds relative to a benchmark (expected steady-state time). The larger the value, the faster the response speed, the smaller the value, the slower the response speed, and the value close to 1 indicates the expected response speed. In order to clearly express the oscillation degree of the control loop, the autocorrelation coefficient of PV is selected to express the correlation between a set of data before and after (themselves and themselves).

4. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: The PV relative performance index, denoted by P r express: where Tr desired represents the expected steady-state time, Tr actual Indicates the actual steady-state time.

5. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: The loop rating table divides the control loop into four levels: excellent, good, fair, and poor based on the coefficient of variation CV, the relative performance index RPI, and the autocorrelation coefficient.

6. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: The diagnostic information library includes fault codes and diagnostic information, and automatically outputs corresponding diagnostic opinions based on the calculation results of various performance indicators for engineers' reference.

7. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: When the control loop self-control rate is ≤60%, it is automatically determined to be in an open-loop state and does not participate in the rating.

8. A PID loop performance rating and diagnosis method based on fluctuation coefficient of variation according to claim 1, characterized in that: It further includes establishing a loop rating history library to record the historical rating information of each loop and provide an optimization reference.

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