CN113538353B - Five-phase asynchronous motor rolling bearing fault diagnosis method based on single-channel diagram data enhancement and migration training residual error network - Google Patents
Five-phase asynchronous motor rolling bearing fault diagnosis method based on single-channel diagram data enhancement and migration training residual error network Download PDFInfo
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Abstract
The application relates to a five-phase asynchronous motor rolling bearing fault diagnosis method based on a single-channel diagram data enhancement and migration training residual error network, which is suitable for the condition of unbalanced small samples and belongs to the technical field of diagnosis. Dividing a sample into a training set and a testing set, and using GASF to code signals of the training set to obtain a single-channel diagram; then taking the single-channel diagram as a sample to train the WGAN-GP network to obtain a generator network corresponding to the sample; and then, constructing a residual classification network by using the residual network convolution layer of the ImageNet pre-training as a feature extraction layer according to the migration training principle. Further, training is performed: training the full connection layer by using the samples generated by the generator network; sequentially training the full-connection layer and the feature extraction layer by using the actual sample as a training set; and fine tuning the whole residual error network. And storing the trained model, and calling to complete diagnosis, wherein the accuracy is not lower than 99.4%.
Description
Technical Field
The application relates to a method for diagnosing faults of a rolling bearing of a five-phase asynchronous motor under the condition of an unbalanced small sample, belonging to the technical field of diagnosis.
Background
Because of the advantages of high reliability, fault-tolerant operation and the like, the five-phase asynchronous motor is already applied to the special fields of ships, submarines and the like, and the rolling bearing is applied to the five-phase asynchronous motor with overwhelming advantages. The rolling bearing is composed of an outer raceway, an inner raceway, a cage and rolling bodies rotating between them. Under normal operating conditions, fatigue failure begins with micro-cracks and gradually propagates, which in turn causes material fragments to fall off, resulting in failure. Therefore, the fault diagnosis of the rolling bearing of the five-phase asynchronous motor has important significance.
At present, vibration signal spectrum analysis is the most accurate and reliable five-phase asynchronous motor rolling bearing fault diagnosis method. The method collects the time domain vibration signals of the rolling bearing and transforms the time domain vibration signals into a frequency domain, and then compares the frequency domain vibration signals with inherent frequency domain vibration characteristics of the rolling bearing so as to judge whether the rolling bearing has faults or not and the types (such as retainer faults, inner raceway faults and outer raceway faults) of the rolling bearing.
Since 2000, machine learning methods have been developed and rapidly applied widely, which opens up new ideas for fault diagnosis. In general, a machine learning-based bearing fault diagnosis method can be divided into three steps: firstly, preprocessing signals to highlight fault characteristics; secondly, selecting a proper model for training and optimizing; and finally, performing fault diagnosis on the actually collected data.
Common machine learning algorithms include support vector machines (Support Vector Machine, SVM), random Forest (RF), etc., which are weak in nonlinearity and difficult to extract deep features, and depend heavily on the feature highlighting effect of the preprocessing algorithm, so that the method is often unable to solve complex classification problems. The five-phase asynchronous motor rolling bearing faults comprise complex conditions such as retainer faults, inner raceway faults, outer raceway faults and interweaving of the outer raceway faults, and the like, and the complex conditions are precisely the complex classification problems. Therefore, the above-described conventional machine learning algorithm is not suitable for the diagnosis of five-phase asynchronous motor rolling bearing failure, or the desired effect cannot be obtained.
More importantly, since high-quality simulation data cannot be generated for bearing faults, but bearing faults in engineering practice are small probability events relative to normal states, the opportunity of acquiring actual bearing fault signals is insufficient, and therefore obtained bearing normal and fault samples are usually unbalanced, and the total amount of fault samples is small. The conventional machine learning algorithm cannot well process the situation of unbalanced small samples, and can not be used for fault diagnosis of the rolling bearing of the five-phase asynchronous motor naturally.
Disclosure of Invention
In order to overcome the defects of the prior art, the application provides a five-phase asynchronous motor rolling bearing fault diagnosis method based on a GASF (Grami angle field) single-channel diagram data enhancement and migration training residual error network, which can generate high-quality and sufficient samples by using a small quantity of vibration signals and realize five-phase asynchronous motor rolling bearing fault diagnosis under unbalanced small samples by using a migration learning residual error classifier network.
The technical scheme adopted by the application is as follows:
a five-phase asynchronous motor rolling bearing fault diagnosis method based on a GASF single-channel diagram data enhancement and migration training residual network comprises the following steps:
a. firstly, a system experiment is carried out, and vibration signals of the rolling bearings of the five-phase asynchronous motor are measured.
This work is done one by one for the four states of the five-phase asynchronous motor rolling bearing normal, cage failure, inner raceway failure, outer raceway failure, and each state covers the load variation (full load, half load, no load) of the five-phase asynchronous motor.
The five different motor rolling bearing is marked with four states of normal, cage fault, inner raceway fault and outer raceway fault as 0, 1,2 and 3 in sequence. By this operation, a large amount of vibration data is acquired. In the normal state (state 0), the data under each loading condition of full load, half load and no load is 20000 groups, and the data is 60000 groups shared by the three loading conditions. Considering that the fault bearing is not suitable for long-time operation, 973 groups of data are respectively measured for three loading conditions of full load, half load and no load under each fault state. For example, for a cage failure (state 1), 973 sets of data are measured under three loading conditions, i.e., full load, half load, and no load, respectively, to obtain a set of cage failure data 2919. The data 2919 sets were also measured as above for the inner race failure (state 2) and the outer race failure (state 3). The sampling duration of each set of data is 40s, and the sampling frequency is 25kHz.
b. At this time, the normal samples are far more than the fault samples, the normal samples are extremely uneven, too many, and the neural network classifier can be caused to learn too many features of the normal samples to ignore the fault samples by directly performing deep learning. The whole data set is downsampled, namely redundant parts of normal samples are randomly discarded, so that the sample numbers in the four states of normal, cage fault, inner raceway fault and outer raceway fault are consistent.
At this time, the normal state data 60000 groups, and the cage failure, the inner race failure, and the outer race failure data 2919 groups. The entire dataset in the four states was randomly downsampled using the random underwaismpler function in the imbalance-learn library of python to obtain the normal sample 2919 set, the cage failure sample 2919 set, the inner race failure sample 2919 set, the outer race failure sample 2919 set. The 11676 sets of samples are summed.
c. For each set of data, an 8-order low-pass butterworth filter was used to filter at a cut-off frequency of 12kHz to reduce noise.
d. For each group of data after filtering, calculating a vibration transient signal v by adopting a 1024-point sliding window method s Effective value V of (2) s By analyzing the trend of the change of the effective value, 4096 point data which is the most stable, i.e. the smallest fluctuation, is extracted as 1 data segment, and 11676 data segments are extracted as samples.
The method for calculating the vibration transient signal v by adopting 1024-point sliding window s Effective value V of (2) s The method of (1) is as follows: selecting the vibration transient signal v s Of the total number of 1024 points, calculate the effective value thereof(k represents the serial number of the sampling point); for the selected v s The continuous 1024 points in (3) are reserved and then the 1023 points are sequentially complemented with v s Later 1 (1025 th) point in (a) to obtain v again s In 1024, again calculate its effective value, and so on, determine v s Trend of effective value change of (a).
e. All samples were divided into training and test sets at a ratio of 70%, 30%.
f. For each sample, a piecewise approximation aggregation algorithm (Piecewise Aggregate Approximation, PAA) proposed by angstrom Mengji o et al was used to reduce the dimensions from 4096 points to 256 points. The dimension reduction process still keeps the labels in one-to-one correspondence with the samples.
g. The sample after dimension reduction is in the following form { x } 1 ,…,x n Time sequence data of n is the data length after dimension reduction, namely 256, x 1 Data representing point 1 of the sample, and so on. Using the formula phi i ＝arccos(x i ) (i=1, 2, …, n) calculating the inverse cosine function value of each point of the sample to obtain the sequence { phi } 1 ,…,φ n (arccosis represents an inverse cosine function, phi 1 Represents x 1 The rest of the analogy), the pseudo-gram matrix is obtained by encoding the inverse cosine function sequence as follows:
wherein phi is i,j ＝φ i +φ j G can be considered as a single channel map. And 11676 samples with the length of 256 points after dimension reduction are encoded by using the Grami angle field algorithm, so as to obtain 11676 single-channel diagrams with the length and width of 256.
h. The application selects WGAN-GP (generating countermeasure network) based on Wasserstein distance with gradient penalty as a generating model. Four WGAN-GP networks were constructed, corresponding to four different training samples, respectively. Training the corresponding generated countermeasure network using samples with a label of 0, observing the sample mass of the generator every 10 cycles, and after training 500 cycles, the model converges, and observing that the generated sample mass meets the requirement. And training the samples corresponding to the labels 1,2 and 3 and generating an countermeasure network according to the flow. After training is completed, a generator of a single-channel graph of the Grami angle field corresponding to four states of 0, 1,2 and 3 is obtained. Using these four generators, 3 ten thousand samples were generated for each of the four states 0, 1,2, 3 of the rolling bearing, for a total of 12 ten thousand generated samples, and as a pre-training set.
The Wasserstein distance, also known as the Earth-Mover distance, is used to measure the distance between two probability distributions, and still reflects the distance between the two probability distributions when the support sets of the two probability distributions do not overlap or overlap very little. The countermeasure generation network model is to make the probability distribution of the generator as close as possible to that of the actual sample, so that the countermeasure generation network based on the distance is easier to train and the pattern of the generated sample is rich, and the gradient penalty term is added to facilitate the acceleration training.
i. And constructing a residual classification network. The method comprises the steps of using a convolution layer of a 50-layer residual error network Resnet50V2 proposed by Microsoft institute and pre-trained on a public face data set ImageNet in tensorsurface 2 as a feature extraction layer, and sequentially connecting a two-dimensional global pooling layer, a full connection layer and an output full connection layer (all provided by tensorsurface 2) to form a residual error classification network suitable for rolling bearing fault classification. Here, tensorflow2 is a deep learning framework that is open-sourced by google.
j. Pre-training is performed for the residual classification network. And (3) firstly freezing the parameters of the feature extraction layer, performing pre-training by using the samples generated in the step (h), and ending the pre-training after reaching the precision index (the test accuracy of 85% is selected as the index in the application).
k. Training the pre-trained network by using an actual training set according to the following steps: 1) Freezing the parameters of the feature extraction layer, and training the full-connection layer; 2) Freezing the full connection layer, and training a convolution feature extraction layer; 3) And finally fine tuning the whole network. In the training process, when the network accuracy is improved less (the improvement of the training index of the classification accuracy after 5 training cycles is lower than 0.01%), the network can be considered to be converged. The application introduces an early-stop mechanism to monitor the convergence condition of the network, the mechanism is provided by a tensorflow2 framework, and the condition that the improvement of training indexes (the application selects the classification accuracy) is small can be monitored in the network training process to judge the convergence of the network and finish the training in advance.
Save the network model as a file (including network structure and weight parameters) in h5 format (provided by the tensorflow2 framework) using the save method of the tensorflow2 framework, and load the file directly and call the file as needed (using the tensorflow. Keras. Model. Load_model function provided by the tensorflow2 framework).
And m, evaluating the trained residual classification network by using the test set.
Filtering in step c, intercepting in step d and obtaining the vibration signal of rolling bearing of actual five-phase asynchronous motor
f, the segmentation approximate aggregation and the GASF code of the step g are input into a trained model (loading the file in h5 format in the step l), and then fault diagnosis can be carried out.
The application has the most remarkable advantages that: by using the GASF algorithm to encode the time sequence signals, the time domain characteristics of the signals are reserved, and the classification performance of the convolutional neural network is improved. And a WGAN-GP network is used for generating a GASF sample graph, so that training samples are fully expanded, and the performance of the classification network under unbalanced data is improved. On the training, a pre-trained residual error network is used as a characteristic extraction layer of the classification network, the training is performed by using a layered training fine-tuning method, the training time cost is greatly reduced on the premise of ensuring the classification accuracy, and the high accuracy is obtained on the test set. The diagnosis model based on the residual classification network can be packaged and stored, and only needs to be called when in actual use, thereby meeting the actual real-time requirements of engineering.
Drawings
FIG. 1 is a flow chart of a GASF algorithm;
FIG. 2 is a schematic illustration of a WGAN-GP configuration;
FIG. 3 is a diagram of a WGAN-GP network configuration;
fig. 4 is a diagram of a residual classification network structure, wherein: the network output is a 1×4 matrix, [ 100 0] corresponds to sample class 0, [ 01 00 ] corresponds to sample class 1, [ 001 0] corresponds to sample class 2, [ 000 1] corresponds to sample class 3;
FIG. 5 is a residual classification network training flow diagram;
FIG. 6 is a GASF diagram of four types of fault actual samples;
FIG. 7 is a loss of WGAN-GP training process generator and ranker for sample 0;
fig. 8 is a sample generated by the WGAN-GP training procedure.
Meaning of each symbol used in the figures: PAA, a piecewise approximation aggregation algorithm; a GASF, glatiramer angle field; GRAM, glam; wasserstein, wasserstein; resnet50V2, a 50-layer residual network proposed by Microsoft institute; WGAN-GP, generates an antagonism network.
Detailed Description
The application is further described below with reference to the accompanying drawings:
the 6206 rolling bearing is arranged on the five-phase asynchronous motor, and a vibration acceleration sensor is arranged at the bearing position to collect vibration signals. And cutting grooves with the width of 1 mm on the inner raceway, the outer raceway and the retainer of the bearing respectively by using a linear cutting machine in advance to simulate the fault bearing. The normal state corresponds to tag 0, the inner race fault state corresponds to tag 1, the outer race fault state corresponds to tag 2, and the cage fault state corresponds to tag 3.
After the five-phase asynchronous motor is started, data 20000 groups are collected respectively under full load, half load and no load conditions according to normal states, and data 60000 groups are shared by three load conditions; aiming at the fault state of the retainer, the data 973 groups are collected under full load, half load and no load conditions respectively, and the data 2919 groups are shared by three load conditions; the data 2919 sets were measured as above for the inner race fault condition, the outer race fault condition. The sampling duration of each set of data is 40s, and the sampling frequency is 25kHz.
And randomly downsampling the acquired unbalanced data set by using a random underwaissang function in an imbalance-learn library of python to acquire a normal sample 2919 group, a retainer fault sample 2919 group, an inner race fault sample 2919 group and an outer race fault sample 2919 group, and totalizing 11676 groups of samples.
All 11676 sets of vibration data were filtered group by group using a low-pass butterworth filter with a filter order of 8 and a cut-off frequency of approximately half the sampling frequency of 12kHz.
For each group of vibration data after filtering, calculating a vibration instantaneous signal v by adopting a 1024-point sliding window method s Effective value V of (2) s By analyzing the trend of the change in the effective value, 4096 point data, which is the most stable, i.e., the smallest fluctuation, is extracted as 1 data segment, 11676 data segments are obtained in total, and taken as samples.
70% of these 11676 samples were used as the training set and 30% were used as the test set, which was approximately balanced with the number of samples of different categories within the training set.
For each sample, the processing is performed according to the GASF algorithm flowchart shown in fig. 1. Firstly, performing data dimension reduction by using a PAA algorithm, and reducing the dimension of a 4096-length time sequence into a 256-length sequence. The 256-length sequence is encoded by using the GASF to obtain single-channel diagrams with length and width of 256, and 11676 single-channel diagrams with the size of 256×256 are obtained. Thus, the pretreatment of the sample is completed. Fig. 6 is a diagram of GASF examples of four types of actual fault samples.
According to the WGAN-GP algorithm and the structure diagram shown in fig. 2 and 3, a WGAN-GP model is constructed based on tensorf low 2. The gradient penalty term is set to 10 and the batch size is set to 8. The WGAN-GP network generator was designed to accept 1 x 100 magnitude random gaussian noise as input, and to connect 8 deconvolution layers backward, with the parameters shown in table 1.
Table 1WGAN-GP network generator parameters
The design scorer accepts 256×256 single-channel pictures as input, and connects 5 convolution layers, a two-dimensional global average pooling layer, a flattening layer, and an output full-connection layer backward, with the convolution layer parameters shown in table 2:
table 2WGAN-GP network scorer parameters
Wherein, relu and LeakyReLU are common neural network activation functions, and are provided by a tensorsurface 2 framework, relu is called a modified linear unit, and LeakyReLU is a special version of relu. The filling modes same and valid are also realized and provided by a two-dimensional convolution function of tensorsurface 2.
The WGAN-GP network was trained using a graphic computing card model RTX-3090 from Nvidia corporation, fig. 7 is a loss curve of the generator and the scorer during the WGAN-GP training process corresponding to sample 0. Using four trained WGAN-GP models, generating 3 ten thousand samples of each category and 12 ten thousand samples in total to form a pre-training set. Fig. 8 is a sample generated at different numbers of cycles during WGAN-GP training.
The residual classification network structure is shown in fig. 4. The input layer accepts 256×256×3 three-channel maps, so the GASF single-channel map is replicated on each channel to construct a three-channel map. The convolutional layer of a Resnet50V2 model pre-trained on the ImageNet is used as a characteristic extraction layer of the residual error classification network, and the upper layer is sequentially connected with a two-dimensional global pooling layer, a full-connection layer and an output layer to form the classification network. Performing network training according to the flow shown in fig. 5, optimizing by using an Adam optimizer provided by a tensorf low2 framework, introducing an early-stopping mechanism, ending training in advance when the network learning speed is reduced to a judgment threshold (the classification accuracy after 5 training cycles are selected to be lower than 0.01% as the judgment threshold) firstly, using 12 ten thousand samples generated by a WGAN-GP network, freezing a feature extraction layer of a residual classification network for training, setting the learning rate to be 0.01, training for 5 cycles, and evaluating the model accuracy on a test set to be about 93% after training; then training only the full-connection layer by using a training set formed by actual samples, setting the learning rate to be 0.005, training for 16 cycles, and evaluating the model accuracy to be about 95% on a test set after training; the full-connection layer is frozen again, only the feature extraction layer is trained on a training set of an actual sample, the learning rate is set to be 0.001, training is carried out for 10 cycles, and the accuracy of the model is estimated to be about 98% on a test set after training; and finally, on a training set formed by actual samples, fine adjustment is carried out on the whole classification network, the learning rate is set to be 0.001, and the training is carried out for 5 cycles. And evaluating the residual classification network subjected to the hierarchical migration training in the steps, wherein the classification accuracy reaches 99.4%, and the table 3 is referred to.
Table 3 residual classification network test set evaluation
And (3) saving the trained residual classification model as a file in h5 format (provided by the tensorf low2 framework) by using a save method for saving the tensorf low2 framework, and directly loading the file when the file is required to be called.
To test the practical effect of the method of the present application, 40 sets of data (10 sets of each state, with the load conditions randomly set to no load or half load or full load) were additionally measured for the normal state (tag 0), the inner race fault state (tag 1), the outer race fault state (tag 2), the cage fault state (tag 3). The 40 sets of data were "blind tested" using the method of the present application and the results are shown in table 4. As can be seen from table 4, the method of the present application has high accuracy.
Table 4 residual classification network "blind test" evaluation
Claims (5)
1. A five-phase asynchronous motor rolling bearing fault diagnosis method based on a single-channel diagram data enhancement and migration training residual error network is characterized by comprising the following steps of: firstly, acquiring vibration transient data of 11676 groups in total according to 25kHz frequency, namely, obtaining 2919 groups from 60000 groups in a normal state through downsampling, wherein the number of the vibration transient data in the four states is the same in each 2919 group in a cage fault state, an inner raceway fault state and an outer raceway fault state, and filtering the vibration transient data group by using an 8-order low-pass Butterworth filter with 12kHz as a cut-off frequency to reduce noise; then, for each group of data after filtering, calculating an effective value by adopting a 1024-point sliding window method, extracting 4096-point data which is the most stable, namely the smallest fluctuation of the effective value as 1 data segment by analyzing the change trend of the effective value, extracting 11676 data segments as samples in total, and dividing all the samples into a training set and a test set according to the proportion of 70% and 30%; for each sample, reducing the dimension by using a piecewise approximation aggregation algorithm, and reducing the dimension from 4096 points to 256 points; using a GASF (gas-insulated plasma enhanced gas) algorithm to encode 11676 samples with the length of 256 points after dimension reduction, namely a glatiramer angle field algorithm, and obtaining 11676 single-channel diagrams with the length and width of 256; selecting a WGAN-GP generating countermeasure network based on Wasserstein distance with gradient penalty as a generating model, using a normal state, a tag 0, a retainer fault state, a tag 1, an inner raceway fault state, a tag 2, an outer raceway fault state and a sample under a tag 3 to train the corresponding WGAN-GP network until convergence, namely obtaining 4 corresponding generators of a single-channel chart of the Grami angle field, respectively using the four generators to generate 3 ten thousands of samples for each of the four states of the rolling bearing 0, 1,2 and 3, and taking the total 12 ten thousands of generated samples as a pre-training set; a convolution layer of a residual network Resnet50V2 which is pre-trained on a public face data set ImageNet in tensorsurface 2 is used as a feature extraction layer, and a two-dimensional global pooling layer, a full connection layer and an output full connection layer are sequentially connected to form a residual classification network suitable for rolling bearing fault classification; pre-training and actual training are sequentially carried out on the residual classification network; the save method of the tensorflow2 framework is used for persisting the network model so as to be convenient to call; evaluating the trained residual classification network by using the test set; the vibration signals of the rolling bearings of the actual five-phase asynchronous motor are filtered, intercepted, segmented, approximately aggregated and GASF coded and then input into a trained model, and fault diagnosis can be carried out.
2. The five-phase asynchronous motor rolling bearing fault diagnosis method based on single-channel diagram data enhancement and migration training residual network according to claim 1, comprising the following steps:
a. experiments are carried out to measure vibration signals of the rolling bearing of the five-phase asynchronous motor, wherein the experiments are carried out in a mode that the rolling bearing of the five-phase asynchronous motor is in a normal state, marked as 0, a fault state of a retainer, marked as 1, a fault state of an inner raceway, marked as 2 and a fault state of an outer raceway, marked as 3, the four states are carried out one by one, each state covers load changes of the five-phase asynchronous motor, namely full load, half load and no load, so that a large amount of vibration data is obtained, for the state 0, the data under each load state of full load, half load and no load are 20000 groups, for the state 0, the three load states of 60000 groups, and for the state 1, the state 2 and the state 3, 973 groups of data are respectively measured under the three load states of full load, half load and no load, so that 2919 groups of data under the states 1,2 and 3 are obtained, and the sampling duration of each group of data is 40s and the sampling frequency is 25kHz;
b. at this time, the normal samples are far more than the fault samples, the normal samples are extremely uneven, the deep learning is directly carried out, the neural network classifier can learn the characteristics of the normal samples too much to ignore the fault samples, so that the whole data set is subjected to downsampling, namely, redundant parts of the normal samples are randomly discarded, so that the number of the samples in the four states of normal, retainer fault, inner race fault and outer race fault is consistent, namely, the normal state data 60000 groups, the retainer fault, inner race fault and outer race fault data are respectively 2919 groups, and the whole data set in the four states is randomly downsampled by using the random undersampler function in the python's imbasic-learn library, so that the normal sample 2919 groups, the retainer fault sample 2919 groups, the inner race fault sample 2919 groups and the outer race fault sample 2919 groups are obtained, and 11676 groups of samples are summed;
c. for each set of data, an 8-order low-pass butterworth filter is used to filter at a cut-off frequency of 12kHz to reduce noise;
d. for each group of data after filtering, calculating the effective value of the vibration transient signal by adopting a 1024-point sliding window method, extracting 4096 point data with the most stable fluctuation as 1 data segment by analyzing the variation trend of the effective value, and extracting 11676 data segments as samples, wherein the method for calculating the effective value of the vibration transient signal by adopting the 1024-point sliding window method comprises the following steps: selecting the vibration transient signal v s Of the total number of 1024 points, calculate the effective value thereofFor the selected v s The continuous 1024 points in (3) are reserved and then the 1023 points are sequentially complemented with v s Later 1 point in (a), 1025 th point, thereby obtaining v again s In 1024, again calculate its effective value, and so on, determine v s Trend of effective value change of (2);
e. dividing all samples into a training set and a testing set according to the proportion of 70% and 30%;
f. for each sample, reducing the dimension by using a piecewise approximate aggregation algorithm, and reducing the dimension from 4096 points to 256 points, wherein the dimension reduction process still keeps the label in one-to-one correspondence with the sample;
g. 11676 samples with length of 256 points after dimension reduction are encoded by using a GASF algorithm to obtain 11676 single-channel graphs with length and width of 256, wherein the samples after dimension reduction are in the following form { x } 1 ,…,x n Time series data of }, n is 256, x 1 Data representing the 1 st point of the sample, and so on, using the formula φ i ＝arccos(x i ) I=1, 2, …, n to calculate the inverse cosine function value of each point of the sample to obtain the sequence { phi } 1 ,…,φ n The inverse cosine function sequence is encoded as follows to obtain a pseudo-gram matrix
Wherein phi is i,j ＝φ i +φ j The matrix is a single-channel diagram;
h. selecting a WGAN-GP generating countermeasure network based on Wasserstein distance with gradient penalty as a generating model, constructing four WGAN-GP networks, respectively corresponding to four different training samples, training the corresponding generating countermeasure network by using samples with labels of 0, observing the sample quality of a generator every 10 cycles, after training 500 cycles, converging the model, observing that the generated sample quality meets the requirement, training samples corresponding to labels of 1,2 and 3 and the generating countermeasure network according to the above flow in sequence, thereby obtaining generators of a single-channel graph of the Grami angle field corresponding to four states of 0, 1,2 and 3 respectively, further generating 3 ten thousand samples for each state of the four states of 0, 1,2 and 3 of the rolling bearing by using the four generators, and taking the total 12 ten thousand generated samples as a pre-training set;
i. constructing a residual classification network, namely using a convolution layer of a 50-layer residual network Resnet50V2 which is pre-trained on a public face data set ImageNet in tensorsurface 2 as a feature extraction layer, and sequentially connecting a two-dimensional global pooling layer, a full connection layer and an output full connection layer to form the residual classification network suitable for rolling bearing fault classification;
j. pretraining the residual classification network: firstly, freezing parameters of a feature extraction layer, performing pre-training by using the samples generated in the step h, ending the pre-training after reaching an accuracy index, and selecting a test accuracy rate of 85% as the index;
k. training the pre-trained network by using an actual training set according to the following steps: 1) freezing parameters of a feature extraction layer, training a full connection layer, 2) freezing the full connection layer, training a convolution feature extraction layer, 3) performing final fine adjustment on the whole network, in the training process, when the network accuracy is improved slightly, namely the improvement of a training index of 'classification accuracy' after 5 training cycles is lower than 0.01%, the network can be considered to be converged, an early-stopping mechanism is introduced to monitor the convergence condition of the network, the mechanism is provided by a tensorsurface 2 framework, and the condition that the improvement of the training index of 'classification accuracy' is small is monitored in the network training process to judge the network convergence and finish training in advance;
the save method of the tensorf low2 framework is used for saving the network model as a file in h5 format, and the file is directly loaded and called when needed;
m, evaluating the trained residual classification network by using the test set;
and n, filtering the vibration signals of the rolling bearings of the actual five different motors in the step c, intercepting in the step d, performing sectional approximate aggregation in the step f, and inputting the signals into a trained model after the GASF codes in the step g.
3. The five-phase asynchronous motor rolling bearing fault diagnosis method based on single-channel diagram data enhancement and migration training residual network according to claim 1, wherein the method is characterized by comprising the following steps of: selecting a WGAN-GP generating countermeasure network based on Wasserstein distance with gradient penalty as a generating model, setting a gradient penalty item as 10, setting a batch size as 8, designing a WGAN-GP network generator to accept random Gaussian noise with the size of 1 multiplied by 100 as input, and connecting 8 deconvolution layers backwards; the design scorer accepts 256×256 single-channel pictures as input, connecting 5 convolution layers backward, a two-dimensional global average pooling layer, a flattening layer, and an output full-connection layer.
4. The five-phase asynchronous motor rolling bearing fault diagnosis method based on single-channel diagram data enhancement and migration training residual network according to claim 1, wherein the method is characterized by comprising the following steps of: and pre-training the residual classification network, firstly freezing parameters of a feature extraction layer, training by using 11676 single-channel graphs with the length and width of 256 obtained by encoding the glatiramer angle field algorithm as samples, and ending the pre-training after reaching the precision index.
5. The five-phase asynchronous motor rolling bearing fault diagnosis method based on single-channel diagram data enhancement and migration training residual network according to claim 1, wherein the method is characterized by comprising the following steps of: performing actual training on the residual classification network, and performing the following steps by using an actual training set: 1) Freezing the parameters of the feature extraction layer, and training the full-connection layer; 2) Freezing the full connection layer, and training a convolution feature extraction layer; 3) And finally fine-tuning the whole network, wherein in the training process, when the network accuracy is improved less, namely the improvement of the training index of the classification accuracy after 5 training cycles is lower than 0.01%, the network can be considered to be converged, an early-stopping mechanism is introduced to monitor the convergence condition of the network, the mechanism is provided by a tensorf low2 framework, and the condition that the improvement of the classification accuracy of the training index is less is monitored in the network training process to judge the network convergence and finish the training in advance.
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