CN111832428A - Data enhancement method applied to strip breakage fault diagnosis of cold rolling mill - Google Patents
Data enhancement method applied to strip breakage fault diagnosis of cold rolling mill Download PDFInfo
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Abstract
The invention provides a data enhancement method applied to strip breakage fault diagnosis of a cold rolling mill, and belongs to the technical field of ferrous metallurgy and fault diagnosis. The method comprises the following steps: collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling, and processing the collected time sequence signals of the plurality of characteristics to generate a two-dimensional fault image set; dividing the fault image set into training data and test data; and training the assistant classification generation countermeasure network by using the training data and the labels corresponding to the training data to obtain a generation model, wherein the trained generation model is used for generating fault images required by belt breakage fault diagnosis. By adopting the method and the device, the training speed of the generated model is improved, and meanwhile, the quality of the generated fault image can be improved, so that the fault image required by the belt breakage fault diagnosis can be generated in a directional mode, and the problem of insufficient fault data in the belt breakage fault diagnosis is solved.
Description
Technical Field
The invention relates to the technical field of ferrous metallurgy and fault diagnosis, in particular to a data enhancement method applied to strip breakage fault diagnosis of a cold rolling mill.
Background
The modern strip steel cold rolling is a high-quality and high-efficiency full-automatic production line flexibly produced according to orders, and strip breakage is one of the most common faults in the cold rolling production line. In case of a belt breakage failure, the light weight causes damage to the equipment. The rolling production efficiency is influenced, and the fire disaster is caused by the twisted belt, so that the personal safety is greatly threatened. The fault diagnosis of the cold rolling broken belt can effectively prevent accidents, inhibit the quality reduction of products and give full play to the flow operation potential to the maximum extent, and has important scientific significance.
The fault diagnosis method based on data driving is a common method in the field of fault diagnosis, and the data quality has great influence on the precision of the method. In the belt breakage fault diagnosis, the belt breakage is influenced by a plurality of factors, so that the data dimensionality is high, the main characteristics are difficult to extract in the diagnosis, and the model training speed is low. In addition, the data of the normal running state of the rolled piece in cold rolling is well obtained, but compared with normal running, the occurrence frequency of faults is not high, so that the fault data is relatively lack, and the data becomes an important factor for restricting the research of belt breakage fault diagnosis based on data driving.
Disclosure of Invention
The embodiment of the invention provides a data enhancement method applied to strip breakage fault diagnosis of a cold rolling mill, which can improve the quality of a generated fault image while improving the training speed of a generated model so as to directionally generate the fault image required by the strip breakage fault diagnosis, thereby solving the problem of insufficient fault data in the strip breakage fault diagnosis. The technical scheme is as follows:
in one aspect, a data enhancement method applied to a strip breakage fault diagnosis of a cold rolling mill is provided, and the method is applied to electronic equipment and comprises the following steps:
collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling, and processing the collected time sequence signals of the plurality of characteristics to generate a two-dimensional fault image set;
dividing the fault image set into training data and test data;
and training the assistant classification generation countermeasure network by using the training data and the labels corresponding to the training data to obtain a generation model, wherein the trained generation model is used for generating fault images required by belt breakage fault diagnosis.
Further, the acquiring time series signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling, and processing the acquired time series signals of the plurality of characteristics to generate a two-dimensional fault image set includes:
collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling;
reducing the dimension of the collected time sequence signals with a plurality of characteristics through a stack self-coding network to obtain one-dimensional time sequence signals;
and generating a two-dimensional gray scale map from the one-dimensional time sequence signal through signal-image conversion to form a two-dimensional fault image set.
Further, the structural connection mode of the stack self-coding network is as follows: input layer → fully connected layer.
Further, the training the countermeasures network generated by the auxiliary classification by using the training data and the labels corresponding to the training data to obtain the generated model further includes:
turning, rotating and denoising the training data obtained by dividing to obtain training data for assisting classification to generate an anti-network;
and inputting the training data of the obtained assistant classification generation countermeasure network and the corresponding labels thereof into the assistant classification generation countermeasure network for training to obtain a generation model.
Further, the auxiliary classification generating the countermeasure network includes: a generator and a discriminator; wherein the content of the first and second substances,
the generator is used for generating a fault image, and the fault image is a two-dimensional gray scale image;
and the discriminator is used for judging the difference between the fault image generated by the generator and the fault image input to the auxiliary classification generation countermeasure network and providing feedback for the generator.
Further, after training the countermeasures network generated by the auxiliary classification by using the training data and the labels corresponding to the training data to obtain a generated model, the method further includes:
generating a fault image required by belt breakage fault diagnosis by using the generated model;
inputting the generated fault image and training data obtained by original division into a two-dimensional convolutional neural network together for training to obtain a belt breakage fault diagnosis model;
the trained belt breakage fault diagnosis model is used for carrying out belt breakage fault diagnosis and outputting a belt breakage fault type.
Further, the structural connection mode of the two-dimensional convolutional neural network is as follows: two-dimensional convolution layer → maximum pooling layer → fully-connected layer → SoftMax layer, where SoftMax represents a normalized exponential function.
Further, after the generated fault image and training data obtained by original division are input into a two-dimensional convolutional neural network together for training, so as to obtain a fault diagnosis model, the method further comprises:
and testing the trained belt breakage fault diagnosis model by using the test data obtained by dividing.
In one aspect, an electronic device is provided, and the electronic device includes a processor and a memory, where at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill.
In one aspect, a computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the data enhancement method applied to the diagnosis of the strip breakage fault of the cold rolling mill.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
in the embodiment of the invention, time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling are collected, and the collected time sequence signals of the plurality of characteristics are processed to generate a two-dimensional fault image set; dividing the fault image set into training data and test data; the training data and the labels corresponding to the training data are used for training the counternetwork generated by the auxiliary classification, the quality of the generated fault image can be improved while the training speed of the generated model is improved, so that the fault image required by the belt breakage fault diagnosis can be generated in a directional mode, and the problem of insufficient fault data in the belt breakage fault diagnosis is solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a data enhancement method applied to strip breakage fault diagnosis of a cold rolling mill according to an embodiment of the present invention;
FIG. 2 is a detailed flowchart of a data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill according to an embodiment of the present invention;
FIG. 3 is a comparison of loss function values before and after data enhancement provided by embodiments of the present invention;
FIG. 4 is a schematic diagram illustrating a comparison of confusion matrices before and after data enhancement according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a data enhancement method applied to a strip breakage fault diagnosis of a cold rolling mill, where the method may be implemented by an electronic device, where the electronic device may be a terminal or a server, and the method includes:
s101, collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling, processing the collected time sequence signals of the plurality of characteristics, and generating a two-dimensional fault image set;
s102, dividing the fault image set into training data and testing data;
s103, training an auxiliary classification generation countermeasure network (ACGANs) by using the training data and the corresponding labels thereof to obtain a generation model, wherein the trained generation model is used for generating fault images required by fault diagnosis of broken belts.
The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill in the embodiment collects time sequence signals of a plurality of characteristics related to the strip breakage fault diagnosis in the cold rolling, processes the collected time sequence signals of the plurality of characteristics and generates a two-dimensional fault image set; dividing the fault image set into training data and test data; the training data and the labels corresponding to the training data are used for training the counternetwork generated by the auxiliary classification, the quality of the generated fault image can be improved while the training speed of the generated model is improved, so that the fault image required by the belt breakage fault diagnosis can be generated in a directional mode, and the problem of insufficient fault data in the belt breakage fault diagnosis is solved.
In this embodiment, the label is a type of a tape break fault, specifically: belt breakage failure of different racks.
In this embodiment, the acquiring time series signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling, and processing the acquired time series signals of the plurality of characteristics to generate a two-dimensional fault image set includes:
a1, collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling;
in this embodiment, as shown in fig. 2, time series signals of 24 characteristics related to the belt breakage fault diagnosis in the cold rolling are collected by N (for example, 24) sensors, so as to obtain a high-dimensional time series signal data matrix.
A2, performing dimensionality reduction on the acquired time sequence signals with multiple characteristics through a stacked auto-encoders network (SAE) to obtain one-dimensional time sequence signals;
in this embodiment, the obtained high-dimensional time sequence signal data matrix is placed in a stacked self-encoding network (SAE) for training, and the high-dimensional time sequence signal data matrix is reduced to a one-dimensional time sequence signal, so as to achieve the effect of information fusion.
In this embodiment, the structural connection manner of the stacked self-coding network (SAE) is as follows: the input layer L0 → the full-link layer L1 → the full-link layer L2 → the full-link layer L3 → the full-link layer L4; wherein, the number of output neurons of the input layer L0 is 30; the number of input neurons and the number of output neurons of the full connection layer L1 are respectively 30 and 10; the number of input neurons and the number of output neurons of the full connection layer L2 are respectively 10 and 1; the number of input neurons and the number of output neurons of the full connection layer L3 are respectively 1 and 10; the number of input neurons and the number of output neurons in the fully connected layer L4 were 10 and 30, respectively.
And A3, generating a two-dimensional gray scale map by the one-dimensional time sequence signals through signal-image conversion, and forming a two-dimensional fault image set.
In this embodiment, the specific way of signal-image conversion is shown in formula (1):
wherein, P (m, n) represents the gray value of the mth row and the nth column in the generated two-dimensional gray scale map; n denotes the size of the generated image as N × N; form L (i) represents the gray value of the ith data point in L; (Max L) and Min (L) respectively represent the maximum value and the minimum value in L, L represents the value of one-dimensional time sequence signal after single sampling, and the length of L is N2(ii) a The rounding function round (x) is used for rounding the data to ensure that the transformed data takes on an integer between 0 and 255.
In this embodiment, the one-dimensional time domain signal may be subjected to signal normalization, signal conversion into a gray value, rounding, signal interception and matrix transformation according to the image size by using the formula (1) to obtain a two-dimensional gray scale map of the one-dimensional time sequence signal, thereby forming a two-dimensional failure image set.
In this embodiment, 80% of the failure image sets constituting the two-dimensional set may be used as training data, and the remaining 20% may be used as test data.
In this embodiment, the training of the countermeasures network generated by auxiliary classification by using the training data and the labels corresponding to the training data to obtain the generative model further includes:
b1, carrying out turning, rotating and noise adding processing on the divided training data to obtain training data for assisting classification to generate the countermeasure network;
in this embodiment, flipping includes horizontally flipping and/or vertically flipping the image.
In this embodiment, the rotation includes rotating the image by 90 ° to the left or right.
In this embodiment, the term "noise adding" refers to adding random gaussian noise to an image, and specifically, adding gaussian noise refers to directly adding a matrix of an image to a number randomly sampled from a gaussian distribution.
In this embodiment, each image in the training data is subjected to the above-mentioned flipping, rotation, and noise addition, so that training data with six times of the original training data number can be obtained, and thus training data with higher diversity for assisting classification and generating the countermeasure network can be obtained.
B2, inputting the training data of the obtained assistant classifying and generating confrontation network and the corresponding label into assistant classifying and generating confrontation network (ACGANs) for training to obtain a generating model.
In this embodiment, the assisted classification generation countermeasure networks (ACGANs) can process two-dimensional images, and therefore, the ACGANs may also be referred to as 2D-ACGANs, as shown in fig. 2.
In this embodiment, the generating the countermeasure networks (ACGANs) by auxiliary classification includes: the generator is used for generating a fault image, the discriminator is used for judging the difference between the fault image generated by the generator and the fault image input to the auxiliary classification generation countermeasure network, and feedback is provided for the generator, and the fault image is a two-dimensional gray scale map. The generator comprises 4 fractional step two-dimensional convolution layers and 4 batch normalization layers, and ReLU functions are used as activation functions except for the fact that the final output layer uses Tanh functions as activation functions. The discriminator comprises 4 two-dimensional convolution layers and 4 batch normalization layers, and the activation function uses LeakyReLU function except the Sigmoid function used by the output layer.
In this embodiment, the two-dimensional convolutional layer has fewer parameters and better characteristic extraction capability on the time-series signal, and meanwhile, the ACGANs described in this embodiment considers the label information of the fault data at the same time, and does not need to train multiple models, so that the training process is simple, and thus, the quality of the generated fault image can be improved while the training speed of generating the models is improved, so that the fault image required by the strip breakage fault diagnosis can be generated in an oriented manner, and the problem of insufficient fault data in the strip breakage fault diagnosis is solved.
In this embodiment, after training the countermeasures network generated by the auxiliary classification by using the training data and the labels corresponding to the training data to obtain a generated model, the method further includes:
generating a fault image required by belt breakage fault diagnosis by using the generated model;
inputting the generated fault image and training data obtained by original division into a two-dimensional (2D) Convolutional Neural Network (CNN) together for training to obtain a fault diagnosis model of fault belt breakage;
the trained belt breakage fault diagnosis model is used for carrying out belt breakage fault diagnosis and outputting a belt breakage fault type.
In this embodiment, the structure of the two-dimensional convolutional neural network includes 11 layers in total, and the specific structural connection manner is as follows: two-dimensional convolution layer L1(5 × 5 × 32) → maximum pooling layer L2(2 × 2) → two-dimensional convolution layer L3(3 × 3 × 64) → maximum pooling layer L4(2 × 2) → two-dimensional convolution layer L5(3 × 3 × 128) → maximum pooling layer L6(2 × 2) → two-dimensional convolution layer L7(3 × 3 × 256) → maximum pooling layer L8(2 × 2) → fully-connected layer L9(2560-768) → fully-connected layer L10(768-10) → SoftMax (normalized exponential function) layer L11; wherein, three parameters in the brackets of the two-dimensional convolution layer respectively represent the length of the convolution kernel, the width of the convolution layer and the number of the convolution kernels; the parameter in the maximum pooling level bracket indicates the size of its window; the parameters in the full connection layer brackets indicate the number of input parameters and the number of output parameters, respectively.
In this embodiment, after the generated fault image and training data obtained by the original division are input to a two-dimensional convolutional neural network together for training to obtain a fault diagnosis model, the method further includes:
and testing the trained belt breakage fault diagnosis model by using the test data obtained by dividing.
In order to verify the effectiveness of the data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill provided by the embodiment of the invention, the characteristic parameters related to the strip breakage faults of the cold rolling mill and the rolling mill in a certain steel mill are collected, the specific composition of the characteristic parameters is shown in table 1, and the fault diagnosis experiment is carried out through the characteristic parameters. In the experiment, fault data and normal data of 4 rolling mills are collected, 24 parameters are totally collected, sampling is carried out at a sampling frequency of 12kHz, 4096 points are taken as a group, and 1000 groups of data are collected.
TABLE 1 characteristic parameters relating to failure diagnosis of strip breakage in cold rolling
| 1 | 1 rack drive side servo valve current |
| 2 | 1 rack operating side servo valve current |
| 3 | Deviation of rolling force (Absolute value) |
| 4 | 2 rolling force deviation of the frame |
| 5 | 2 rack drive side servo valve current |
| 6 | 2 rack operating side servo valve current |
| 7 | 3 rack transmission side servo valve current |
| 8 | 3 rack operating side servo valve current |
| 9 | Deviation of rolling force |
| 10 | 4 rack transmission side servo valve current |
| 11 | 4 rack operating side servo valve current |
| 12 | 4 rolling force deviation |
| 13 | 1 actual tension value of the frame |
| 14 | 1 tension offset value of the frame |
| 15 | 2 actual tension value of the frame |
| 16 | 2 tension offset value of frame |
| 17 | 3 actual tension value of the frame |
| 18 | Tension deviation value of 3 machine frame |
| 19 | 4 actual tension value of the frame |
| 20 | 4 tension offset value of frame |
| 21 | 1 machine frame motor current |
| 22 | Motor current of 2 machine frame |
| 23 | Motor current of 3 machine frame |
| 24 | 4 machine frame motor current |
Fig. 3(a) shows a variation trend of a loss function of a fault diagnosis model under no data enhancement, and it can be seen that a training loss function is finally approximately converged to 0, and a test loss function always oscillates around 0.5, which indicates that an overfitting phenomenon occurs in the model due to insufficient training data; fig. 3(b) is a variation trend of a loss function of the broken belt fault diagnosis model after the data enhancement method provided by the embodiment of the present invention, where a loss function value of the broken belt fault diagnosis model is approximately converged to 0 on both training data and test data, which shows that an overfitting phenomenon substantially disappears after the data enhancement.
Fig. 4(a), (b) are schematic diagrams comparing confusion matrixes for performing data enhancement front and rear broken belt fault diagnosis, wherein data on a symmetry axis indicates that the health state is correctly identified in proportion to all test data, and the rest data of each row is in proportion to other health states which are incorrectly identified. It can be clearly seen that after data enhancement, the recognition accuracy of the 3-rack is improved from 92.5% to 99%, which is improved by 6.5%, and the total average accuracy is also improved from 95% to 99.5%.
Fig. 5 is a schematic structural diagram of an electronic device 600 according to an embodiment of the present invention, where the electronic device 600 may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 601 and one or more memories 602, where the memory 602 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 601 to implement the data enhancement method applied to the diagnosis of the strip breakage fault of the cold rolling mill.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, is also provided that includes instructions executable by a processor in a terminal to perform the data enhancement method described above as applied to cold rolling mill strip break fault diagnosis. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A data enhancement method applied to strip breakage fault diagnosis of a cold rolling mill is characterized by comprising the following steps:
collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling, and processing the collected time sequence signals of the plurality of characteristics to generate a two-dimensional fault image set;
dividing the fault image set into training data and test data;
and training the assistant classification generation countermeasure network by using the training data and the labels corresponding to the training data to obtain a generation model, wherein the trained generation model is used for generating fault images required by belt breakage fault diagnosis.
2. The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill according to claim 1, wherein the step of collecting time sequence signals of a plurality of characteristics related to the strip breakage fault diagnosis in the cold rolling, and the step of processing the collected time sequence signals of the plurality of characteristics to generate a two-dimensional fault image set comprises the following steps:
collecting time sequence signals of a plurality of characteristics related to belt breakage fault diagnosis in cold rolling;
reducing the dimension of the collected time sequence signals with a plurality of characteristics through a stack self-coding network to obtain one-dimensional time sequence signals;
and generating a two-dimensional gray scale map from the one-dimensional time sequence signal through signal-image conversion to form a two-dimensional fault image set.
3. The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill as claimed in claim 2, wherein the structural connection mode of the stack self-coding network is as follows: input layer → fully connected layer.
4. The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill as claimed in claim 1, wherein the training of the auxiliary classification generation countermeasure network by using the training data and the corresponding labels thereof to obtain the generation model further comprises:
turning, rotating and denoising the training data obtained by dividing to obtain training data for assisting classification to generate an anti-network;
and inputting the training data of the obtained assistant classification generation countermeasure network and the corresponding labels thereof into the assistant classification generation countermeasure network for training to obtain a generation model.
5. The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill as claimed in claim 1, wherein the auxiliary classification generation countermeasure network comprises: a generator and a discriminator; wherein the content of the first and second substances,
the generator is used for generating a fault image, and the fault image is a two-dimensional gray scale image;
and the discriminator is used for judging the difference between the fault image generated by the generator and the fault image input to the auxiliary classification generation countermeasure network and providing feedback for the generator.
6. The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill as claimed in claim 1, wherein after training the auxiliary classification generation countermeasure network by using the training data and the corresponding labels thereof to obtain a generation model, the method further comprises:
generating a fault image required by belt breakage fault diagnosis by using the generated model;
inputting the generated fault image and training data obtained by original division into a two-dimensional convolutional neural network together for training to obtain a belt breakage fault diagnosis model;
the trained belt breakage fault diagnosis model is used for carrying out belt breakage fault diagnosis and outputting a belt breakage fault type.
7. The data enhancement method applied to the fault diagnosis of the broken strip of the cold rolling mill according to claim 1, wherein the structural connection mode of the two-dimensional convolution neural network is as follows: two-dimensional convolution layer → maximum pooling layer → fully-connected layer → SoftMax layer, where SoftMax represents a normalized exponential function.
8. The data enhancement method applied to the strip breakage fault diagnosis of the cold rolling mill according to claim 1, wherein after the generated fault image and the training data obtained by the original division are input into a two-dimensional convolutional neural network together for training to obtain a fault diagnosis model, the method further comprises the following steps:
and testing the trained belt breakage fault diagnosis model by using the test data obtained by dividing.
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