CN116433085A - Performance evaluation method of rolling process control system - Google Patents

Performance evaluation method of rolling process control system Download PDF

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CN116433085A
CN116433085A CN202310333831.XA CN202310333831A CN116433085A CN 116433085 A CN116433085 A CN 116433085A CN 202310333831 A CN202310333831 A CN 202310333831A CN 116433085 A CN116433085 A CN 116433085A
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孙杰
李树
刘云霄
乔继柱
丁肇印
李梦琴
彭文
丁敬国
张殿华
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东北大学
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Abstract

The invention provides a performance evaluation method of a rolling process control system, and relates to the technical field of rolling process control. Firstly, collecting strip steel production data in a rolling process including a normal production process; grouping the acquired strip steel production data in the rolling process according to time sequences to obtain a plurality of groups of time sequence data; then using a trend removal fluctuation analysis algorithm to process the strip steel production data in the grouped rolling process to solve the Hurst index of the strip steel production data; and finally, defining a performance index based on the Hurst index according to the calculated Hurst index value, and evaluating the performance of the current rolling process control system by the performance index. The method does not use any priori knowledge of related system parameters, fully utilizes a large amount of data in the rolling production process, realizes the performance evaluation of the rolling control system, and is convenient for more clearly judging the performance of the current controller in the production process.

Description

Performance evaluation method of rolling process control system
Technical Field
The invention relates to the technical field of rolling process control, in particular to a performance evaluation method of a rolling process control system.
Background
With the development of modern industry, the quantity and quality of the cold and hot rolled plate strip steel required by each industry department are higher and higher. In the rolling production process, thickness control is a core control function for improving and ensuring thickness accuracy of products. Because the rolling control system has a plurality of and complex loops, and meanwhile, the operation working conditions of the rolling control system are often changed and various unknown interferences exist, most production lines are basically in a long-term operation state since the production line is automatically put into production at present, and the performance of the thickness controller is reduced due to the change of the operation working conditions of the system, the lack of maintenance of the control system and the like, so that the control requirement of a set target cannot be met. In order to find out and adjust the loops with poor performance in time, the control system is ensured to operate efficiently and well, and the performance evaluation technology of the rolling control system has important scientific significance and application value.
Because the structure of a controlled object in the actual rolling industrial control process is complex and the operation conditions are variable, an accurate system mathematical model is difficult to build. The conventional minimum variance reference compares the output mean square error with the theoretical minimum variance, and it is difficult to obtain a process time delay and conventional output data to obtain a feedback invariant term of the system output variance independent of the controller form and parameters.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a performance evaluation method of a rolling process control system, which utilizes a large amount of data in the rolling production process to realize the evaluation of the performance of the rolling control system and is convenient for more clearly judging the performance of the current controller in the production process.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for evaluating the performance of a rolling process control system, comprising the steps of:
step 1: collecting strip steel production data in a rolling process including a normal production process; the strip steel production data in the rolling process comprise strip steel inlet and outlet thickness data of each pass;
step 2: grouping the collected strip steel production data in the rolling process according to time sequences to obtain a plurality of groups of time sequence data;
step 3: the detrack fluctuation analysis algorithm is used for processing and solving Hurst indexes of strip steel production data in the grouped rolling process, and the specific calculation steps are as follows:
step 3.1: determining an autocorrelation sequence Y (j) of each set of time sequences: the original time series data is mapped to the autocorrelation sequence after subtracting the average value thereof from the original time series data, and the following formula is shown:
Figure BDA0004155781220000011
wherein y (i) is the ith group of original time series data,
Figure BDA0004155781220000021
y (j) is the autocorrelation sequence of the original time series data of the ith group, j is the grouping number of the strip steel production data, and N is the total number of the strip steel production data in the collected rolling process;
step 3.2: performing least square curve fitting on the autocorrelation sequence of each set of time series data over a window length n; dividing the autocorrelation sequence Y (j) into d windows with length of n, and the original window data time sequence is Y m (i) M=1, the combination of the first and second components, d, and a first order least squares fit is performed on the data within each window, obtaining a least square fitting curve corresponding to each window
Figure BDA0004155781220000022
Step 3.3: calculating a time sequence Y for each window m (i) Fitting curve corresponding to least square
Figure BDA0004155781220000023
As an intermediate variable for calculating the root mean square fluctuation function of the window +.>
Figure BDA0004155781220000024
Figure BDA0004155781220000025
Wherein Y is m (k) For the time series of the original window data,
Figure BDA0004155781220000026
is Y m (k) A least square fitting curve in the corresponding window;
step 3.4: the root mean square fluctuation function F (n) of the window is calculated at a window length of n, as shown in the following formula:
Figure BDA0004155781220000027
step 3.5: repeating the steps 3.1-3.4 under the condition of changing the window length n until all values in the window length range are calculated; wherein the window length n is selected so that the dispersion points obtained by calculating log F (n) approximate a linear function;
step 3.6: drawing a double-logarithmic graph by taking logn as an abscissa and logF (n) as an ordinate, fitting double-logarithmic scattered points by least square, and calculating the slope of a curve, wherein the obtained slope is the estimated value of the Hurst index corresponding to the group of time sequence data;
step 3.7: averaging the estimated values of the Hurst indexes corresponding to the time series data of each group to obtain Hurst index values corresponding to all production data;
step 4: defining a performance index eta based on the Hurst index according to the calculated Hurst index value H The following formula is shown:
Figure BDA0004155781220000028
the performance of the current rolling process control system is evaluated by the performance index, which is between 0,1, and the more toward 1, the more excellent the current control system performance level.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: according to the performance evaluation method for the rolling process control system, provided by the invention, the Hurst index is calculated through the trend-removal fluctuation analysis algorithm, so that the problem of solving a mathematical model of a controlled object in the minimum variance method is avoided, and the control performance quality of the current controller is effectively measured. The method does not use any priori knowledge of related system parameters, fully utilizes a large amount of data in the rolling production process, realizes the performance evaluation of the rolling control system, and is convenient for more clearly judging the performance of the current controller in the production process. Meanwhile, the method has the advantages of high running speed, low model checking dependence, high calculation precision and small calculation amount, can utilize a large amount of production process data to calculate and solve, can be directly realized on a computer through programming, does not need to be put into cost, and can be widely popularized to single-frame cold rolling production.
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FIG. 1 is a flow chart of a method for evaluating the performance of a rolling process control system according to an embodiment of the present invention;
fig. 2 is a block diagram of rolling mill outlet thickness production data according to an embodiment of the present invention, wherein (a) is packet data 1, (b) is packet data 2, (c) is packet data 3, (d) is packet data 4, (e) is packet data 5, (f) is packet data 6,
fig. 3 is a log-log graph corresponding to each packet data provided in the embodiment of the present invention, (a) is a graph corresponding to packet data 1, (b) is a graph corresponding to packet data 2, (c) is a graph corresponding to packet data 3, (d) is a graph corresponding to packet data 4, (e) is a graph corresponding to packet data 5, and (f) is a graph corresponding to packet data 6.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In the embodiment, a single-stand cold-rolling mill is taken as an example, and the performance evaluation method of the rolling process control system based on the Hurst index is adopted to evaluate the performance of the rolling process control system of the single-stand cold-rolling mill. The evaluation adopts the data in the production process of a single-frame cold rolling mill, the thickness set value of the strip steel incoming material is 2mm, and the thickness set value of the five-pass strip steel outlet is 0.2mm.
In this embodiment, a method for evaluating performance of a rolling process control system, as shown in fig. 1, includes the following steps:
step 1: collecting strip steel production data in a rolling process including a normal production process; the strip steel production data in the rolling process comprise strip steel inlet and outlet thickness data of each pass;
in the embodiment, the outlet thickness production data of the cold rolling fifth pass mill is collected for research, 30000 cases of normal production process data are collected, and the interval between the collected data is 0.02s.
Step 2: grouping the collected strip steel production data in the rolling process according to time sequences to obtain a plurality of groups of time sequence data;
in this embodiment, each time series includes 5000 data points (i.e., n=5000), and the packet data is shown in fig. 2;
step 3: the strip steel production data in the grouping rolling process is processed by using a detrack fluctuation analysis algorithm (DFAalgoritm) to solve the Hurst index (strip steel thickness or tension) of the strip steel production data, and the specific calculation steps are as follows:
step 3.1: determining an autocorrelation sequence Y (j) of each set of time sequences: the original time series data is mapped to the autocorrelation sequence after subtracting the average value thereof from the original time series data, and the following formula is shown:
Figure BDA0004155781220000041
wherein y (i) is the ith group of original time series data,
Figure BDA0004155781220000042
y (j) is the autocorrelation sequence of the original time series data of the ith group, j is the grouping number of the strip steel production data, and N is the total number of the strip steel production data in the collected rolling process;
step 3.2: performing a least squares curve fit on the autocorrelation sequence of each set of time series data over a window length n: dividing the autocorrelation sequence Y (j) into d windows with length of n, and the original window data time sequence is Y m (i) M=1, the combination of the first and second components, d, and a first order least squares fit is performed on the data within each window, obtaining a least square fitting curve corresponding to each window
Figure BDA0004155781220000043
Step 3.3: calculating a time sequence Y for each window m (i) Fitting curve corresponding to least square
Figure BDA0004155781220000044
As an intermediate variable for calculating the root mean square fluctuation function of the window +.>
Figure BDA0004155781220000045
Figure BDA0004155781220000046
Wherein Y is m (k) For the time series of the original window data,
Figure BDA0004155781220000047
is Y m (k) A least square fitting curve in the corresponding window;
step 3.4: the root mean square fluctuation function F (n) of the window is calculated at a window length of n, as shown in the following formula:
Figure BDA0004155781220000048
step 3.5: repeating the steps 3.1-3.4 under the condition of changing the window length n until all values in the window length range are calculated; wherein the window length n is selected so that the dispersion points obtained by calculating log F (n) approximate a linear function; in this embodiment, the value range of the window length n is 10 to 2500;
step 3.6: drawing a double-logarithmic graph by taking logn as an abscissa and logF (n) as an ordinate, fitting double-logarithmic scattered points by least square and calculating the slope of a curve, wherein the obtained slope is an estimated value of a Hurst index corresponding to the group of time sequence data as shown in figure 3;
step 3.7: averaging the estimated values of the Hurst indexes corresponding to the time series data of each group to obtain Hurst index values corresponding to all production data;
step 4: defining a performance index eta based on the Hurst index according to the calculated Hurst index value H The following formula is shown:
Figure BDA0004155781220000051
the performance of the current rolling process control system is evaluated by the performance index, which is between 0,1, and the more toward 1 the better the current control system performance level.
In this embodiment, h=0.82, η is calculated H =0.68, from which the current control system performance level can be estimated to be good.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.

Claims (7)

1. A performance evaluation method of a rolling process control system is characterized by comprising the following steps of: the method comprises the following steps: step 1: collecting strip steel production data in a rolling process including a normal production process;
step 2: grouping the collected strip steel production data in the rolling process according to time sequences to obtain a plurality of groups of time sequence data;
step 3: processing the strip steel production data in the grouped rolling process by using a trend removal fluctuation analysis algorithm to solve the Hurst index of the strip steel production data;
step 4: according to the calculated Hurst index value, defining a performance index based on the Hurst index, and evaluating the performance of the current rolling process control system by the performance index.
2. The method for evaluating the performance of a rolling process control system according to claim 1, wherein: the strip steel production data in the rolling process comprise strip steel inlet and outlet thickness data of each pass of the rolling mill.
3. The method for evaluating the performance of a rolling process control system according to claim 1, wherein: the specific method of the step 3 is as follows:
step 3.1: determining an autocorrelation sequence Y (j) of each set of time sequences;
step 3.2: performing least square curve fitting on the autocorrelation sequence of each set of time series data over a window length n;
step 3.3: calculating a time sequence Y for each window m (i) Fitting curve corresponding to least square
Figure FDA0004155781210000011
As an intermediate variable for calculating the root mean square fluctuation function of the window +.>
Figure FDA0004155781210000012
Figure FDA0004155781210000013
Wherein Y is m (k) For the time series of the original window data,
Figure FDA0004155781210000014
is Y m (k) A least square fitting curve in the corresponding window;
step 3.4: the root mean square fluctuation function F (n) of the window is calculated at a window length of n, as shown in the following formula:
Figure FDA0004155781210000015
step 3.5: repeating the steps 3.1-3.4 under the condition of changing the window length n until all values in the window length range are calculated;
step 3.6: drawing a double-logarithmic graph by taking logn as an abscissa and logF (n) as an ordinate, fitting double-logarithmic scattered points by least square, and calculating the slope of a curve, wherein the obtained slope is the estimated value of the Hurst index corresponding to the group of time sequence data;
step 3.7: and averaging the estimated values of the Hurst indexes corresponding to the time series data of each group to obtain the Hurst index values corresponding to all production data.
4. A method of evaluating the performance of a rolling process control system according to claim 3, wherein: step 3.1 is to use the original time series data to subtract the average value, and then map the original time series data to the autocorrelation sequence, as shown in the following formula:
Figure FDA0004155781210000021
wherein y (i) is the ith group of original time series data,
Figure FDA0004155781210000022
and Y (j) is the autocorrelation sequence of the original time series data of the ith group, j is the grouping number of the strip steel production data, and N is the total number of the strip steel production data in the acquired rolling process.
5. The method for evaluating the performance of a rolling process control system according to claim 4, wherein: step 3.2 divides the autocorrelation sequence Y (j) into d windows with length of n, and the original window data time sequence is Y m (i) M=1, the combination of the first and second components, d, and a first order least squares fit is performed on the data within each window, obtaining a least square fitting curve corresponding to each window
Figure FDA0004155781210000023
6. The method for evaluating the performance of a rolling process control system according to claim 5, wherein: the window length n is chosen to be such that the calculated dispersion of logF (n) approximates a linear function.
7. The method for evaluating the performance of a rolling process control system according to claim 6, wherein: the performance index based on the Hurst index is shown as the following formula:
Figure FDA0004155781210000024
wherein eta H Is a performance index based on Hurst index, the index value is between [0,1]And the more trendThe more excellent the current control system performance level is explained to 1.
CN202310333831.XA 2023-03-30 2023-03-30 Performance evaluation method of rolling process control system Pending CN116433085A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117519067A (en) * 2023-10-20 2024-02-06 东北大学 Multi-frame control performance evaluation method in continuous rolling process
CN117983668A (en) * 2024-04-07 2024-05-07 东北大学 Hot rolling process thickness loop tension optimization control method based on performance evaluation

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117519067A (en) * 2023-10-20 2024-02-06 东北大学 Multi-frame control performance evaluation method in continuous rolling process
CN117983668A (en) * 2024-04-07 2024-05-07 东北大学 Hot rolling process thickness loop tension optimization control method based on performance evaluation

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