CN112001421B - Industrial control loop state identification method - Google Patents

Industrial control loop state identification method Download PDF

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CN112001421B
CN112001421B CN202010729517.XA CN202010729517A CN112001421B CN 112001421 B CN112001421 B CN 112001421B CN 202010729517 A CN202010729517 A CN 202010729517A CN 112001421 B CN112001421 B CN 112001421B
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control loop
data
image
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industrial control
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CN112001421A (en
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刘勇
马新强
张可鑫
杨建党
徐晋鸿
黄羿
王埸
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Zhejiang University ZJU
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/20—Analysing
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    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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Abstract

The invention discloses a state identification method of an industrial control loop, which comprises the following steps: s1: acquiring annotated industrial control loop image data; s2: constructing two-stage image feature learners based on convolution kernels with different sizes; s3: and identifying the state of the control loop according to the two-stage image feature learning device and the classifier based on a multi-time scale integrated decision-making method. The method replaces the traditional mode of carrying out complicated manual feature extraction and processing on industrial process data, has no requirement on the length of the data, can quickly, effectively and automatically extract potential features in control loop data, and can be used for control loop abnormity identification and performance evaluation.

Description

Industrial control loop state identification method
Technical Field
The invention relates to the technical field of industrial control, in particular to an industrial control loop state identification method.
Background
With the continuous development of scientific technology, an analog feedback control loop based on a PID (proportional-Integral-Derivative) controller, a PI (proportional-Integral-Derivative) controller, a PD (proportional-Derivative) controller, a state observer, a phase compensator, various filters, a feedforward controller, a cascade loop controller, and a 2 × 2 multi-loop controller is generally applied to the fields of industrial production, automation control, and the like to form a corresponding feedback control system. For a feedback control system, how to identify the state of a control loop in the feedback control system is an extremely important technical problem.
The in-depth analysis of the control loop data is beneficial to monitoring and optimizing the control loop. In particular, there are a number of control loops for temperature, pressure, flow, etc. in the process industry. The effective monitoring and analysis of the loop data is beneficial to improving the performance of the whole loop and reducing the energy consumption and saving the cost. When a control loop has just been put into use, its performance is often better. However, the degradation of the performance of the control loop may occur due to aging of the actuator in the control loop or characteristic change of the control object. In actual production, stopping the control loop operation and detecting it is not a feasible solution, and therefore it is very valuable to mine features in the loop from production data to evaluate the operating state of the control loop.
The existing control loop identification only utilizes observation characteristics on images, actually processed data or industrial process time sequence data, the characteristics of the control loop in the traditional method are often manually extracted according to the experience of an operator or the production mechanism of the control loop, and different characteristics are often required to reflect different states, so that the working difficulty of field operators is greatly increased.
Disclosure of Invention
The invention provides an industrial control loop state identification method based on image feature automatic learning, aiming at solving the problem that the existing control loop needs manual identification, and the method has important significance for analyzing the operation state of the control loop.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for identifying the state of the industrial control loop comprises the following steps:
s1: acquiring annotated industrial control loop image data;
s2: constructing two-stage image feature learners based on convolution kernels with different sizes;
s3: and identifying the state of the control loop according to the two-stage image feature learning device and the classifier based on a multi-time scale integrated decision-making method.
In the method of the present invention, the step S1 specifically includes the steps of:
s101: acquiring historical image data of an industrial production process, calibrating the working condition corresponding to the image according to the working condition, and expressing an image data set as T { (img)1,c1)，(img2,c2)，...，(imgN,cN) Where N denotes the total number of acquired images and the annotated class is denoted ciWhere i ∈ 1iE, 1, C, C represents the total number of categories;
s102: unifying pixel sizes of all images in the image dataset.
In the method of the present invention, the step S2 specifically includes the steps of:
s201: constructing a first-stage feature learner based on convolution;
s202: and constructing a second-stage feature learner based on the convolution.
In the method of the present invention, the pixel size is 32 × 32.
In the method of the present invention, the first stage feature learner is composed of 8 convolution kernels of 5 × 5 and maximum pooling level of 2 × 2, and the second stage feature learner is composed of 20 convolution kernels of 3 × 3 and maximum pooling level of 2 × 2.
In the method of the present invention, the step S3 specifically includes the steps of:
s301: given new control loop data, set to
Figure BDA0002602666920000021
And
Figure BDA0002602666920000022
t represents the length of the data, and the set of time scales is set as L epsilon { L1,l2,...,lMIn which liRepresenting the ith time scale, obtaining data of OP and PV data at the ith time scale as
Figure BDA0002602666920000023
And
Figure BDA0002602666920000024
will be provided with
Figure BDA0002602666920000025
And
Figure BDA0002602666920000026
the formed image is set as the ith image, and M images can be finally obtained by each control loop on the assumption that M different time scales exist;
s302: m recognition results are obtained through a two-stage image feature learning device and are expressed as
Figure BDA0002602666920000027
M is an odd number;
s303: and obtaining a category identification result by adopting a strategy of integrating voting.
In the method of the present invention, the category identification result takes the category with a large number of occurrences as the category identification result.
Compared with the prior art, the method replaces the traditional mode of carrying out complicated manual feature extraction and processing on industrial process data, obtains a control loop mode recognizer with higher practicability and accuracy by training by inputting historical image data into a designed convolutional neural network, can judge the running state of a control loop more reliably based on a multi-time scale recognition strategy, can enhance the robustness and reliability of a final result, has no requirement on the length of the data, and can quickly, effectively and automatically extract potential features in the control loop data, thereby further realizing effective recognition of different running modes of the industrial control loop.
Drawings
FIG. 1 is a flow chart of a method for identifying the status of an industrial control loop according to the present invention;
FIG. 2 is an exemplary graph of image data collected by an industrial process of the present invention;
FIG. 3 is an exemplary diagram of the features automatically extracted by the first level learner of the present invention;
FIG. 4 is an exemplary diagram of the features automatically extracted by the second stage learner of the present invention;
FIG. 5 is a diagram of an exemplary multi-time scale image data under a single loop.
Detailed Description
The following embodiments of the present invention are provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The invention provides a state identification method of an industrial control loop, which comprises the following steps as shown in figure 1:
s1: acquiring annotated industrial control loop image data;
s2: constructing two-stage image feature learners based on convolution kernels with different sizes;
s3: and identifying the state of the control loop according to the two-stage image feature learning device and the classifier based on a multi-time scale integrated decision-making method.
Further, in an embodiment of the present invention, the step S1 specifically includes the steps of:
s101: acquiring historical image data of an industrial production process, calibrating the working condition corresponding to the image according to the working condition, and expressing an image data set as T { (img)1,c1)，(img2,c2)，...，(imgN,cN)}，Where N represents the total number of acquired images and the annotated class is denoted ciWhere i ∈ 1iE, 1, C, C represents the total number of categories;
s102: unifying pixel sizes of all images in the image data set.
Specifically, taking the detection of control loop viscosity as an example, the control loop viscosity is mainly caused by insensitivity of a valve, and severe viscosity can cause oscillation of a loop to cause performance degradation, and image data with category labels is obtained from industrial production historical data, as shown in fig. 2 (the abscissa represents OP, the ordinate represents PV, the loop 1 is labeled as a viscous loop, and the loop 2 is labeled as a non-viscous loop). The image pixel size is set to 32 x 32. Fig. 2 shows sample examples of a viscous loop image and a non-viscous loop image.
Further, in an embodiment of the present invention, the step S2 specifically includes the steps of:
s201: constructing a first-stage feature learner based on convolution;
s202: and constructing a second-stage feature learner based on the convolution.
Specifically, the first-stage feature learner is composed of 8 convolution kernels of 5 × 5 and a maximum pooling layer of 2 × 2, and the first-stage feature learner uses a larger convolution kernel size to learn a larger range or more obvious feature in the image, as shown in fig. 3, taking a loop 1 as an example, the upper row of fig. 3 represents the feature extracted by the convolution kernel, and the lower row represents the feature passing through the maximum pooling layer.
The second level of feature learner, which consists of 20 convolution kernels of 3 x 3 and 2 x 2 of the largest pooling layer, uses a smaller convolution kernel size to learn smaller or finer features in the image, with the extracted features shown in fig. 4.
By adopting two stages of convolution kernels with different sizes, the characteristics of large scale and small scale in the image can be captured simultaneously. But also different numbers of convolution kernels can ensure that as many features as possible are learned. Let the input image be characterized as input (1,32,32), and the features extracted by the convolution kernel are
Figure BDA0002602666920000041
Wherein
Figure BDA0002602666920000042
Representing the weighting factor on the output jth channel. The parameters of the two-stage feature learner are shown in the following table.
Figure BDA0002602666920000043
The features extracted by the two-stage learner are represented as feats (N)2,H2,W2) Where N2 denotes the number of convolution kernels of the second stage feature learner, H2 and W2 respectively characterize the final extracted image height and width. And inputting the group of characteristics into a designed full-connection neural network classifier, wherein the output of the full-connection neural network is the corresponding control loop type. Before the network is fully connected, a dropout layer is added, and the dropout layer has the function of randomly screening the features to avoid generating redundant features. Where the final output layer parameter C represents the total number of categories of the acquired image.
Figure BDA0002602666920000044
It can be seen that through two-stage feature extraction, image features under different scales can be obtained at the same time, and multiple features can also be obtained under the same scale. And inputting the extracted features into a designed fully-connected neural network classifier, and finally outputting whether a control loop is sticky.
Further, in an embodiment of the present invention, the step S3 specifically includes the steps of:
s301: given new control loop data, set to
Figure BDA0002602666920000051
And
Figure BDA0002602666920000052
t represents the length of the data, and the set of time scales is set as L epsilon { L1,l2,...,lMH, wherein liRepresenting the ith time scale, obtaining data of OP and PV data at the ith time scale as
Figure BDA0002602666920000053
And
Figure BDA0002602666920000054
will be provided with
Figure BDA0002602666920000055
And
Figure BDA0002602666920000056
the formed image is set as the ith image, and M images can be finally obtained by each control loop on the assumption that M different time scales exist;
s302: m recognition results are obtained through a two-stage image feature learning device and are expressed as
Figure BDA0002602666920000057
M is an odd number;
s303: and obtaining a category identification result by adopting a strategy of integrating voting.
And finally judging the identification result, and adopting an integrated voting strategy. The final decision result can be expressed as:
Figure BDA0002602666920000058
where N and S represent different categories, respectively, | N | and | S | represent the number of occurrences of each category in the final result R, respectively. The final result takes the category with more occurrence times as the recognition result.
When a new control loop is obtained, the control loop OP/PV data is first plotted into OP-PV images at multiple time scales. An example of a multi-time scale image is shown in fig. 5. This example selects 5 different time scales. The 5 images are identified simultaneously by the model in step S2, and the identification result is shown as loop 1 in table 1, while the identification of the remaining 4 control loops is summarized in the present embodiment as shown in the following table.
Loop circuit Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5 Final decision
1 Viscous glue Viscous glue Viscous glue Viscous glue Viscous glue Viscous glue
2 Viscous glue Viscous glue Non-stick Viscous glue Viscous glue Viscous glue
3 Viscous glue Non-stick Viscous glue Non-stick Non-stick Non-stick
4 Viscous glue Viscous glue Viscous glue Viscous glue Viscous glue Viscous glue
5 Viscous glue Viscous glue Viscous glue Non-stick Non-stick Viscous glue
The method replaces the traditional mode of carrying out complicated manual feature extraction and processing on industrial process data, obtains a control loop mode recognizer with higher practicability and accuracy by inputting historical image data into a designed convolutional neural network, can judge the running state of a control loop more reliably based on a multi-time scale recognition strategy, can enhance the robustness and reliability of a final result, has no requirement on the length of the data, and can quickly, effectively and automatically extract potential features in the control loop data, thereby further realizing the effective recognition of different running modes of the industrial control loop.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (6)

1. An industrial control loop state identification method is characterized by comprising the following steps:
s1: acquiring annotated industrial control loop image data;
s2: constructing two-stage image feature learners based on convolution kernels with different sizes;
s3: identifying the state of a control loop according to the two-stage image feature learning device and the classifier based on a multi-time scale integrated decision-making method;
the step S3 specifically includes the steps of:
s301: given new control loop data, set to
Figure FDA0003601632530000011
And
Figure FDA0003601632530000012
t represents the length of the data, and the set of time scales is set as L epsilon { L1,l2,…,lMIn which liRepresenting the ith time scale, obtaining data of OP and PV data at the ith time scale as
Figure FDA0003601632530000013
And
Figure FDA0003601632530000014
will be provided with
Figure FDA0003601632530000015
And
Figure FDA0003601632530000016
the formed image is set as the ith image, and M images can be finally obtained by each control loop on the assumption that M different time scales exist;
s302: m recognition results are obtained through a two-stage image feature learning device and are expressed as
Figure FDA0003601632530000017
M is an odd number;
s303: and obtaining a category identification result by adopting a strategy of integrating voting.
2. The industrial control loop state identification method according to claim 1,
the step S1 specifically includes the steps of:
s101: acquiring historical image data of an industrial production process, calibrating the working condition corresponding to the image according to the working condition, and expressing an image data set as T { (img)1,c1)，(img2,c2)，…，(imgN,cN) Where N denotes the total number of acquired images and the annotated class is denoted ci'Where i' is e.1i'E, 1, C, C represents the total number of categories;
s102: unifying pixel sizes of all images in the image dataset.
3. The industrial control loop state identification method according to claim 2,
the step S2 specifically includes the steps of:
s201: constructing a first-stage feature learner based on convolution;
s202: and constructing a second-stage feature learner based on the convolution.
4. The industrial control loop state identification method according to claim 2,
the pixel size is 32 x 32.
5. The industrial control loop state identification method according to claim 3,
the first stage of feature learner is composed of 8 convolution kernels of 5 × 5 and maximum pooling level of 2 × 2, and the second stage of feature learner is composed of 20 convolution kernels of 3 × 3 and maximum pooling level of 2 × 2.
6. The industrial control loop state identification method according to claim 5,
and the category identification result takes the category with more occurrence times as the category identification result.
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