CN111476263B - Bearing defect identification method based on SDAE and improved GWO-SVM - Google Patents
Bearing defect identification method based on SDAE and improved GWO-SVM Download PDFInfo
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Abstract
The invention relates to a bearing defect identification method based on SDAE and an improved GWO-SVM, which comprises the following steps: 1. collecting vibration signals of the bearing under normal conditions and fault conditions, and preprocessing data; 2. constructing a four-layer initial stacking denoising self-coding SDAE, and training an initial stacking denoising self-coding network SDAE;3. establishing an improved GWO-SVM classifier model, extracting the deepest features of the SDAE, and training an improved GWO-SVM classifier; SDAE fine-tunes and retrains the improved GWO-SVM classifier until classification accuracy is met. The invention has the advantages that: according to the invention, the data characteristic extraction and the classification model are combined, so that the accuracy of recognition is improved; through the noise-removing self-coding training SDAE network, not only the characteristics of the original data are learned, but also the degraded characteristics after being destroyed are learned, so that the SDAE network has stronger generalization and robustness; the improved GWO method is better in terms of convergence speed and accuracy.
Description
Technical Field
The invention belongs to the field of fault identification, and particularly relates to a bearing defect identification method based on SDAE and an improved GWO-SVM.
Background
It is counted that among the types of faults that occur in the mechanical equipment, bearing faults account for 30% of the weight. If the fault occurs on large-scale ship equipment, serious safety accidents and economic losses can be caused; if the fault occurs on the aeroengine, the air parking can be caused, and the tragic of the death of the aircraft is destroyed. The fault defect of the bearing is timely found, reliable equipment defect information is provided for maintenance personnel, so that reasonable maintenance strategies are formulated, the accident occurrence probability can be reduced, and the service life and the production efficiency of the equipment are improved.
The traditional defect identification method and means play a good role in single system faults, but have poor diagnosis effect on composite faults and large equipment. The artificial intelligent diagnosis does not depend on a specific diagnosis object and a specific mathematical model, only trains a defect identification model through learning historical data, and then judges and positions the defect type by combining online data, thereby realizing online diagnosis of large equipment. Since intelligent fault diagnosis of complex systems often requires more abstraction, deeper networks are required.
At present, defect identification of equipment is mostly completed by monitoring parameters such as vibration signals related to the equipment and utilizing methods such as feature extraction, information fusion, pattern identification and the like. For example, fault diagnosis is achieved with wavelet transformation and support vector machines, and defect identification is achieved with kernel principal component analysis and support vector machines. However, the above two methods analyze only a single vibration signal, and the vibration signal is often an aliasing of a plurality of signals, and is greatly interfered by other signals, so that it is difficult to extract effective features. The method combines wavelet analysis and D-S evidence theory to realize defect identification, and combines wavelet packet, kernel principal component analysis and SVM to realize defect identification of a rotor and a bearing, and the two methods combine vibration signals and current signals to make up for the defects of a single signal, but the characteristic extraction process needs a great deal of priori knowledge, abundant signal processing theory and practical experience as support, and the characteristic extraction process and the defect identification process are completely isolated, so that the data characteristic extraction and model training are not connected.
In recent years, SVM has been increasingly applied to various classification and regression problems, and can solve the learning problem of small samples of equipment data and the uncertainty problem of evaluation results. However, the generalization ability of SVR is mainly determined by penalty coefficient C and kernel function parameter σ, and the selection of the two parameters affects the recognition rate. The intelligent optimization algorithm such as genetic algorithm and particle swarm algorithm and the improved algorithm thereof are common methods for optimizing SVM parameters at present.
The gray wolf optimization algorithm (Grey Wolf Optimizer, GWO) was a group intelligent optimization algorithm proposed by university of griffish in australia mirjalli et al in 2014. The algorithm simulates the gray wolf group level system and predation behavior in the nature, and achieves the purpose of efficient optimization through the processes of group searching, surrounding, pursuing attack hunting objects and the like. Therefore, the method is suitable for SVM parameter optimization. However, the standard GWO algorithm has the characteristics of low post convergence rate, easy sinking into local optimum, low recognition accuracy and the like, so that the method needs to be improved.
Disclosure of Invention
The invention aims to provide a bearing defect identification method based on SDAE and an improved GWO-SVM, which can improve the convergence speed and the identification precision and solve the defect that the bearing is easy to fall into local optimum.
In order to solve the technical problems, the technical scheme of the invention is as follows: a bearing defect identification method based on SDAE and improved GWO-SVM is characterized in that: the method comprises the following steps:
step 1: collecting monitoring data of the bearing under normal conditions and different defect conditions, preprocessing the data, extracting and normalizing the processed data, and randomly dividing each type of preprocessed features into a training sample and a testing sample according to a certain proportion;
step 2: establishing a stacked denoising self-coding network SDAE with the network layer number of 4, wherein the stacked denoising self-coding network SDAE is used for extracting characteristics of training data and test data and training an initial stacked denoising self-coding network SDAE;
step 3: establishing an improved GWO-SVM classifier model, and taking the deepest data features extracted from the SDAE of the coding network through initial stacking denoising as GWO-SVM classifier input to train the classifier;
step 4: the stacked denoising self-coding network SDAE parameters are finely adjusted by using a back propagation BP algorithm, a gradient descent algorithm is applied to update weights, and the GWO-SVM classifier is retrained and improved until classification accuracy is met;
step 5: and obtaining a data extraction and defect identification integral model based on the SDAE and the improved GWO-SVM classifier according to the steps, and realizing deep feature extraction and defect identification of the bearing by using the model.
Further, the data in the step 1 is subjected to feature extraction, and the feature extraction comprises 13 time domain features, 4 frequency domain features and 5 time-frequency domain features which are extracted by empirical mode decomposition; the time domain features comprise 7 dimensionless time domain features of skewness, mean value, variance, peak value, minimum value, peak-to-peak value and mean square value, and 6 dimensionless time domain features of kurtosis, peak value factor, pulse factor, waveform factor, margin factor and skewness factor; the frequency domain features comprise mean square frequency, center of gravity frequency, frequency variance and standard frequency variance; the time-frequency domain features include the first 4 IMF energy indices and the total energy index.
Further, the structure of the 4-layer stacked denoising self-coding network SDAE in the step 2 from bottom to top is [44-22-11-5 ]; the training process of the stacked denoising self-coding network SDAE adopts layer-by-layer stacking learning, and after the unsupervised training of each denoising self-coder DAE is completed, the output of an implicit layer is used as the input of the next denoising self-coder DAE, namely the training of the first denoising self-coder DAE1 is completed; taking the data characteristic 1 of the hidden layer as the input of a second denoising self-encoder DAE2, and performing unsupervised training on the DAE2 to obtain the data characteristic 2; taking the data characteristic 2 as the input of a third denoising self-encoder DAE3, and performing unsupervised training on the DAE3 to obtain the data characteristic 3; taking the data characteristic 3 as the input of a third denoising self-encoder DAE4, and performing unsupervised training on the DAE4 to obtain the data characteristic 4; the feature data 4 would be input as a GWO-SVM classifier.
Further, in the stacked denoising self-coding network SDAE, an improved GWO-SVM classifier is added after the last feature representation layer of the SDAE, and the whole trained network can simultaneously realize the feature extraction and defect identification tasks of data.
Further, in the step 3, the improved GWO-SVM classifier model optimizes the SVM penalty factor C and the kernel function parameter σ by using an improved wolf algorithm GWO, and specifically comprises the following steps:
step 1: initializing parameters improving GWO, e.g. population size N, maximum number of iterations t max Initial value a of distance control parameter a initial And a termination value a final The method comprises the steps of carrying out a first treatment on the surface of the Initializing a, A and C; initializing the population to obtain N initial wolf positions X= [ X ] 1 ,X 2 ,…,X N ]Wherein X is i ＝(C i ,σ i )；
Step 2: calculating fitness value of each individual of the population, sequencing the fitness values, and respectively recording that three individuals with optimal fitness are alpha, beta and delta, and the corresponding positions are X α ,X β ,X δ ；
Step 3: updating the positions of other wolf individuals in the population;
step 4: updating the values of the convergence factor a and the parameter A, C;
step 5: calculating individual fitness f (X) i (t)) and sorting the sirius population in descending order, differentially mutating the last 10% of the sirius individuals, and calculating the fitness f (X) of the individuals after mutation i ' (t)) and comparing the fitness of individuals before and after mutation, if f (X) i '(t))≥f(X i (t)) replacing the individual before mutation with the individual after mutation, iff(X i '(t))＜f(X i (t)) retaining the pre-variant individual;
step 6: judging whether the maximum iteration times t are reached max If t is not reached max T=t+1 and returns to step 2; if so, stopping iteration and returning to the optimal individual;
step 7: and obtaining an optimal punishment factor C and an optimal kernel function parameter sigma according to the optimal gray wolf position, and performing defect identification by utilizing an improved GWO-SVM classifier.
Further, the population initialization formula in the step 1 is as follows:
wherein the method comprises the steps ofAnd x is E [0,1 ]]。
Further, the population position update in the step 3 satisfies the following conditions:
wherein A and C are coefficient vectors, X i (t) is the current individual location, X i (t+1) is the individual position after iterative update.
Further, the update of a in the step 4 satisfies:
t is the current iteration number, random variable λ=0.01;
the coefficient vector a, C update satisfies:
A＝2a·r 1 -a
C＝2·r 2
wherein r is 1 And r 2 Is [0,1]Random numbers within.
Further, the differential variation formula in the step 5 is:
X'(t)＝r[X α -X(t)]-r[X s (t)-X(t)]，X s (t) wherein r is [0,1 ] for a randomly selected individual one of the wealth of the population]Is a random number of (a) in the memory.
Further, the fine tuning in the step 4 specifically includes the following steps:
step one: inputting training samples into an initial stacking denoising self-coding network SDAE which is pre-trained, extracting top-level features, and taking the top-level features as training samples for supporting an improved GWO-SVM classifier to obtain an SVM optimal penalty factor C and an optimal kernel function parameter sigma;
step two: fine tuning network parameters by using a back propagation BP algorithm, and updating weights by using a gradient descent algorithm;
step three: inputting training samples into a stack denoising self-coding network SDAE with fine adjustment completed, extracting top-level characteristics, retraining and improving a GWO-SVM classifier to obtain an optimal penalty factor C and an optimal kernel function parameter sigma of the SVM classifier;
step four: inputting the top-level features into the trained optimal SVM classifier, judging whether a termination condition is met, if so, finishing fine tuning, stopping iteration, finishing SDAE network and improved GWO-SVM classifier training, and if not, jumping to the second step.
The invention has the advantages that: according to the bearing defect identification method based on the SDAE and the improved GWO-SVM, the stacked denoising self-coding network SDAE is adopted to extract the characteristics of the data, so that the self-adaptive mining of high-dimensional deep fault characteristics is realized, the problems that the types of the faults of the bearing are multiple, the fault characteristics are difficult to extract and the like are solved, the SDAE not only learns the characteristics of the original data, but also learns the degraded characteristics after being damaged, so that the method has stronger generalization and robustness; the data characteristic extraction and defect recognition process are combined, the connection of the data characteristic extraction and defect recognition process is established, and the defects that a large amount of priori knowledge, abundant signal processing theory and practical experience are needed as supports in the characteristic extraction process are overcome; the GWO algorithm is improved, the convergence speed and the recognition accuracy of the algorithm are improved, the defect that the algorithm is easy to fall into local optimum is overcome, the SVM parameter is optimized by utilizing the improvement GWO, and the defect recognition accuracy is improved.
Drawings
The invention will be described in further detail with reference to the drawings and the detailed description.
FIG. 1 is a flow chart of a method for identifying bearing defects based on SDAE and modified GWO-SVM according to the present invention.
Fig. 2 is a schematic diagram of the operation of the first denoising self-encoder according to the present invention.
Fig. 3 is a block diagram of a stacked denoising self-encoder SDAE according to the present invention.
Fig. 4 is a flow chart of the stacked denoising self-encoder SDAE tuning according to the present invention.
FIG. 5 is a flowchart of a modified GWO-SVM according to the present invention.
Detailed Description
The following examples will provide those skilled in the art with a more complete understanding of the present invention and are not intended to limit the invention to the embodiments described.
Examples
The bearing defect identification method based on SDAE and improved GWO-SVM in the embodiment mainly comprises the following steps as shown in FIG. 1:
step 1: collecting monitoring data of the bearing under normal conditions and different defect conditions, preprocessing the data, and extracting features of the processed data, wherein the feature extraction comprises 13 time domain features, 4 frequency domain features and 5 time-frequency domain features extracted by Empirical Mode Decomposition (EMD); wherein the time domain features comprise 7 dimensional time domain features of skewness, mean, variance, peak value, minimum value, peak-to-peak value and mean square value (see table 1); kurtosis, peak factor, pulse factor, waveform factor, margin factor, skew factor 6 dimensionless time domain features (see table 2); the frequency domain features include mean square frequency, center of gravity frequency, frequency variance, standard frequency variance (see table 3); the time-frequency domain features include the first 4 IMF energy indices and the total energy index.
Table 1 dimensional time domain characteristic parameters
Table 2 dimensionless time domain characterization parameters
TABLE 3 frequency domain characterization parameters
And carrying out normalization processing on the data, setting labels for normal conditions and different defect conditions of the equipment, wherein each label corresponds to a respective defect state, and randomly dividing each type of preprocessed characteristics into a training sample and a test sample according to a certain proportion.
Step 2: and establishing a stacked denoising self-coding network SDAE with the network layer number of 4, wherein the stacked denoising self-coding network SDAE is used for extracting characteristics of training data and test data and training an initial stacked denoising self-coding network SDAE.
Step 3: an improved GWO-SVM classifier is built and the deepest data features extracted from the coding network SDAE through initial stacking denoising are used as GWO-SVM classifier inputs to train the classifier.
Step 4: and fine tuning network parameters by using a back propagation BP algorithm, updating weights by using a gradient descent algorithm, and retraining the improved GWO-SVM classifier until classification accuracy is met.
Step 5: and obtaining a data extraction and defect identification integral model based on the SDAE and the improved GWO-SVM classifier according to the steps, and realizing deep feature extraction and defect identification of the bearing by using the model.
Referring to fig. 2 and 3, the first denoising self-encoder is composed of an encoder, a decoder and an hidden layer; the denoising self-encoder maps x' to q D (x '|x) adding a random noise to the original input x, and adding noise q to the data set D to obtain a damaged sample x', wherein D is the data set, and q is the random noise; at this time, the encoder input is a corrupted sample, which is encoded by the encoding function f θ Mapping to the hidden layer y can be expressed as:
y 1 ＝f θ1 (x')＝s(W 1 x'+b 1 ) (1)
where s is a mapping function, W 1 Is the coding weight of the first denoising self-encoder, b 1 Is the coding bias of the first denoising self-encoder, and theta 1 is the pre-trained coding parameter of the first denoising self-encoder; output y of first denoising self-encoder hidden layer 1 Reconstructing through a decoding function:
z 1 ＝g θ1 '(y 1 )＝S'(W 1 'y+b 1 ') (2)
wherein S' is a mapping function, W 1 ' is the decoding weight of the first denoising self-encoder, b 1 'is the decoding bias of the first denoising auto-encoder, θ1' is the decoding parameter of the first denoising auto-encoder. For each input x i Mapping to a y 1 i Then get the reconstruction function z 1 i All parameters are optimized continuously, and the error of minimizing input and reconstruction decoding is obtained:
L(x i ,z 1 i )＝||x i -z 1 i || 2 (3)
the working principles of the second denoising self-encoder, the third denoising self-encoder and the fourth denoising self-encoder are the same as those of the first denoising self-encoder.
In the embodiment, the structure of the 4-layer stacked denoising self-coding network SDAE in the step 2 from bottom to top is [44-22-11-5 ]; the training process of the stacked denoising self-coding network SDAE adopts layer-by-layer stacking learning, and after the unsupervised training of each denoising self-coder DAE is completed, the output of an implicit layer is used as the input of the next denoising self-coder DAE, namely the training of the first denoising self-coder DAE1 is completed; taking the data characteristic 1 of the hidden layer as the input of a second denoising self-encoder DAE2, and performing unsupervised training on the DAE2 to obtain the data characteristic 2; taking the data characteristic 2 as the input of a third denoising self-encoder DAE3, and performing unsupervised training on the DAE3 to obtain the data characteristic 3; taking the data characteristic 3 as the input of a third denoising self-encoder DAE4, and performing unsupervised training on the DAE4 to obtain the data characteristic 4; the data feature 4 would be input as a GWO-SVM classifier.
Referring to fig. 4, the stacked denoising self-coding network SDAE tuning process includes the following steps:
step one: inputting training samples into an initial stacking denoising self-coding network SDAE which is pre-trained, extracting top-level features, and taking the top-level features as training samples for supporting an improved GWO-SVM classifier to obtain an SVM optimal penalty factor C and an optimal kernel function parameter sigma;
step two: fine tuning network parameters by using a back propagation BP algorithm, and updating weights by using a gradient descent algorithm;
step three: inputting training samples into a stack denoising self-coding network SDAE with fine adjustment completed, extracting top-level characteristics, retraining and improving a GWO-SVM classifier to obtain an optimal penalty factor C and an optimal kernel function parameter sigma of the SVM classifier;
step four: inputting the top-level features into the trained optimal SVM classifier, judging whether a termination condition is met, if so, finishing fine tuning, stopping iteration, finishing SDAE network and improved GWO-SVM classifier training, and if not, jumping to the second step.
Referring to fig. 5, the improved GWO-SVM classifier utilizes an improved gray wolf algorithm GWO to optimize an SVM penalty factor C and a kernel function parameter σ, and specifically comprises the following steps:
step 1: initializing parameters of improvement GWO, the gray wolf population scale N and the maximum iteration number t max Initial value a of distance control parameter a initial And a termination value a final The method comprises the steps of carrying out a first treatment on the surface of the Initializing parameters a, A and C; let i (i=1,) N]) The position of the Pechamus at the t-th iteration is X i (t) the gray wolf group can be divided into a first-neck wolf alpha, a second-neck wolf beta, a captain wolf delta and other individual wolves omega according to a hierarchical mechanism from high to low; all the individual wolves were initialized using the following formula:
wherein the method comprises the steps ofAnd x is E [0,1 ]]K is the maximum chaotic iteration step number, and N initial gray wolf positions X= [ X ] are obtained through initialization 1 ,X 2 ,…,X N ]Wherein X is i ＝(C i ,σ i ) The position of the gray wolf is the SVM penalty factor C and the kernel function parameter sigma which need to be optimized i 。
Step 2: calculating fitness value f (X i (t)) and sorting the individuals, and respectively recording that three individuals with optimal fitness are alpha, beta and delta, and the corresponding positions are X α ,X β ,X δ The corresponding fitness value is f (X α (t)),f(X β (t)),f(X δ (t))。
Step 3: updating the positions of other wolf individuals in the population and the position update satisfies:
where a and C are coefficient vectors.
Step 4: updating the values of the convergence factor a and the parameter A, C, the updating satisfying:
t is the current iteration number, random variable λ=0.01.
A＝2a·r 1 -a (10)
C＝2·r 2 (11)
Wherein r is 1 And r 2 Is [0,1]Random numbers within.
Step 5: calculating individual fitness f (X) i (t)) and ordering the wolf population in descending order, differential variation of the last 10% of wolf individuals:
X i '(t)＝r[X α -X i (t)]-r[X s (t)-X i (t)] (12)
wherein X is s (t) is a single gray wolf selected randomly from the population, r is [0,1 ]]Is a random number of (a) in the memory. Calculating fitness f (X) of individuals after mutation i ' (t)) and comparing the fitness of individuals before and after mutation, if f (X) i '(t))≥f(X i (t)) replacing the individual before mutation with the individual after mutation, if f (X) i '(t))＜f(X i (t)) and preserving the pre-variant individuals.
Step 6: judging whether the maximum iteration times t are reached max If t is not reached max T=t+1 and returns to step 2;if so, stopping iteration and returning to the optimal individual;
step 7: and obtaining an optimal punishment factor C and an optimal kernel function parameter sigma according to the optimal gray wolf position, and performing defect identification by utilizing an improved GWO-SVM classifier.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A bearing defect identification method based on SDAE and an improved GWO-SVM is characterized in that: the method comprises the following steps:
step 1: collecting monitoring data of the bearing under normal conditions and different defect conditions, preprocessing the data, extracting and normalizing the processed data, and randomly dividing each type of preprocessed features into a training sample and a testing sample according to a certain proportion;
step 2: establishing a stacked denoising self-coding network SDAE with the network layer number of 4, wherein the stacked denoising self-coding network SDAE is used for extracting characteristics of training data and test data and training an initial stacked denoising self-coding network SDAE;
step 3: establishing an improved GWO-SVM classifier model, and taking the deepest data features extracted from the SDAE of the coding network through initial stacking denoising as GWO-SVM classifier input to train the classifier;
step 4: the stacked denoising self-coding network SDAE parameters are finely adjusted by using a back propagation BP algorithm, a gradient descent algorithm is applied to update weights, and the GWO-SVM classifier is retrained and improved until classification accuracy is met;
step 5: obtaining a data extraction and defect identification integral model based on the SDAE and the improved GWO-SVM classifier according to the steps, and realizing deep feature extraction and defect identification of the bearing by using the model;
in the step 3, the improved GWO-SVM classifier model optimizes the SVM penalty factor C and the kernel function parameters by using an improved gray wolf algorithm GWO, and the specific steps are as follows:
step 1: initializing parameters of the improvement GWO, such as population size N, maximum iteration number, initial value and termination value of the distance control parameter; initializing a, A and C; initializing a population to obtain N initial wolf positions, wherein;
step 2: calculating fitness values of each individual of the population, sequencing the fitness values, and respectively recording three individuals with optimal fitness as corresponding positions;
step 3: updating the positions of other wolf individuals in the population;
step 4: updating the convergence factor and the value of the parameter A, C;
step 5: calculating individual fitness, sorting the gray wolf population in a descending order, carrying out differential mutation on the gray wolf individuals arranged at the rear 10%, calculating the fitness of the individuals after mutation, comparing the individual fitness before and after mutation, if the individuals before mutation are replaced by the gray wolf individuals after mutation, if the individuals before mutation are reserved;
step 6: judging whether the maximum iteration times are reached, if not, t=t+1, returning to the step 2, and if so, stopping iteration, and returning to the optimal individual;
step 7: obtaining an optimal punishment factor C and an optimal kernel function parameter according to the optimal gray wolf position, and performing defect identification by utilizing an improved GWO-SVM classifier;
the fine tuning in the step 4 comprises the following specific steps:
step one: inputting training samples into an initial stacking denoising self-coding network SDAE which is pre-trained, extracting top-level features as training samples of a support improvement GWO-SVM classifier, and obtaining an SVM optimal penalty factor C and optimal kernel function parameters;
step two: fine tuning network parameters by using a back propagation BP algorithm, and updating weights by using a gradient descent algorithm;
step three: inputting training samples into a stack denoising self-coding network SDAE with fine adjustment completed, extracting top-level characteristics, retraining and improving a GWO-SVM classifier to obtain an optimal penalty factor C and optimal kernel function parameters of the SVM classifier;
step four: inputting the top-level features into the trained optimal SVM classifier, judging whether a termination condition is met, if so, finishing fine tuning, stopping iteration, finishing SDAE network and improved GWO-SVM classifier training, and if not, jumping to the second step.
2. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: the data in the step 1 is subjected to feature extraction, and the feature extraction comprises 13 time domain features, 4 frequency domain features and 5 time-frequency domain features which are extracted by empirical mode decomposition; the time domain features comprise 7 dimensionless time domain features of skewness, mean value, variance, peak value, minimum value, peak-to-peak value and mean square value, and 6 dimensionless time domain features of kurtosis, peak value factor, pulse factor, waveform factor, margin factor and skewness factor; the frequency domain features comprise mean square frequency, center of gravity frequency, frequency variance and standard frequency variance; the time-frequency domain features include the first 4 IMF energy indices and the total energy index.
3. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: the structure of the 4-layer stacked denoising self-coding network SDAE in the step 2 from bottom to top is [44-22-11-5 ]; the training process of the stacked denoising self-coding network SDAE adopts layer-by-layer stacking learning, and after the unsupervised training of each denoising self-coder DAE is completed, the output of an implicit layer is used as the input of the next denoising self-coder DAE, namely the training of the first denoising self-coder DAE1 is completed; taking the data characteristic 1 of the hidden layer as the input of a second denoising self-encoder DAE2, and performing unsupervised training on the DAE2 to obtain the data characteristic 2; taking the data characteristic 2 as the input of a third denoising self-encoder DAE3, and performing unsupervised training on the DAE3 to obtain the data characteristic 3; taking the data characteristic 3 as the input of a third denoising self-encoder DAE4, and performing unsupervised training on the DAE4 to obtain the data characteristic 4; the feature data 4 would be input as a GWO-SVM classifier.
4. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: in the stacked denoising self-coding network SDAE, an improved GWO-SVM classifier is added after the last feature representation layer of the SDAE, and the whole trained network can simultaneously realize the feature extraction and defect identification tasks of data.
5. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: the population initialization formula in the step 1 is as follows:
wherein the method comprises the steps ofAnd x is E [0,1 ]]。
6. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: the updating of the population position in the step 3 meets the following conditions:
wherein A and C are coefficient vectors, X i (t) is the current individual location, X i (t+1) is the individual position after iterative update.
7. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: the update of a in the step 4 satisfies the following conditions:
t is the current iteration number, random variable λ=0.01;
the coefficient vector a, C update satisfies:
A＝2a·r 1 -a
C＝2·r 2
wherein r is 1 And r 2 Is [0,1]Random numbers within.
8. The method for identifying bearing defects based on SDAE and modified GWO-SVM according to claim 1, characterized in that: the differential variation formula in the step 5 is as follows: x' (t) =r [ X ] α -X(t)]-r[X s (t)-X(t)]，X s (t) wherein r is [0,1 ] for a randomly selected individual one of the wealth of the population]Is a random number of (a) in the memory.
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