CN114372492A - Interpretable rolling bearing fault diagnosis method - Google Patents

Interpretable rolling bearing fault diagnosis method Download PDF

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CN114372492A
CN114372492A CN202111676747.5A CN202111676747A CN114372492A CN 114372492 A CN114372492 A CN 114372492A CN 202111676747 A CN202111676747 A CN 202111676747A CN 114372492 A CN114372492 A CN 114372492A
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刘之航
丁康
何国林
蒋飞
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South China University of Technology SCUT
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Abstract

The invention discloses an interpretable rolling bearing fault diagnosis method, which comprises the following steps: collecting one-dimensional time sequence signals of the rolling bearing, expanding samples, establishing an initial 1D-CNN-BilSTM neural network model, adding a Grad-CAM + + interpretation layer to the neural network model, and establishing the neural network model with convolution interpretation capability. And training the neural network model by using various one-dimensional fault data to obtain a model with fault diagnosis capability, and performing fault diagnosis on the rolling bearing through the fault diagnosis model. The invention explains the characteristic extraction process of the neural network with CNN as the basic structure, adds the BilSTM, utilizes the characteristic of bidirectional analysis capability, realizes better diagnosis precision, and improves the noise immunity and robustness of the fault diagnosis neural network model.

Description

Interpretable rolling bearing fault diagnosis method
Technical Field
The invention belongs to the field of fault diagnosis and signal processing of rotating machinery, and particularly relates to an interpretable rolling bearing fault diagnosis method.
Background
Rolling bearings are widely used in various industries, such as vehicles and aerospace. The health of the rolling bearing is the basic guarantee for the stable operation of the whole equipment, so the method has obvious significance for monitoring and comprehensively evaluating the running state of the rolling bearing. The rolling bearing is easy to generate fatigue damage and performance decline, the safety and reliability of the whole system are influenced, and the research on fault diagnosis and health management has important theoretical research significance and engineering application value.
The traditional fault diagnosis method has two problems, one is that more expert knowledge related to mechanical engineering and statistics is needed, and the other is that along with the more and more complex operation conditions of the rolling bearing, the traditional fault diagnosis method usually does not perform ideally and has no self-adaptability, so a method based on feature learning is needed to be found for fault feature extraction and diagnosis research.
With the continuous development of artificial intelligence and deep learning, the convolutional neural network has the advantages of no pressure on high-dimensional data processing, no need of prior knowledge and automatic feature extraction and the like by virtue of the shared convolutional kernel, and is widely applied in many fields. Most CNNs are not interpretable, however, resulting in the process often suffering from "black boxes". In addition, when the noise is large, the diagnostic performance of the conventional CNN is not satisfactory, so it is extremely important to propose an interpretable bearing failure diagnostic method having good noise resistance.
The defects of the bearing detection method (CN201910985498.4) based on the convolutional neural network in the prior art are as follows:
1. in the derivation process of the Grad-CAM, the weight is estimated according to the size Z of the feature map, so that the relationship between the weight and the size of the feature map is large, and the result is influenced. In addition, the method of the present invention does not activate well for the same class that appears many times in the input image.
2. The noise resistance effect thereof is unknown.
Disclosure of Invention
The invention aims to provide an interpretable fault diagnosis method for a rolling bearing, aiming at the defects of the prior art. The method aims to solve the technical problems that more prior knowledge is needed, the CNN process is frequently subjected to scaling, the noise immunity of a common CNN is poor and the like in the traditional fault diagnosis method.
The invention is realized by at least one of the following technical schemes.
An interpretable rolling bearing fault diagnosis method includes the following steps:
s1, collecting a one-dimensional time sequence signal of the rolling bearing and expanding a sample;
s2, establishing an initial 1D-CNN-BilSTM neural network model;
s3, adding an explanation layer to the neural network model;
s4, establishing a neural network model with convolution interpretation capability;
s5, training the neural network model by using various one-dimensional fault data to obtain a model with fault diagnosis capability;
and S6, carrying out fault diagnosis on the rolling bearing through the fault diagnosis model.
Further, step S1 includes the steps of:
s11, collecting normal, outer ring fault, inner ring fault and rolling body fault one-dimensional time signals collected by the acceleration sensor;
s12, expanding the one-dimensional time signal in an overlapped sampling mode, and dividing a data sample into a training set and a test set;
and S13, dividing the data sample into different training sets and test sets for multiple times in a K-fold cross validation mode, and finally averaging results to improve the robustness of the model.
Further, the structure of the initial 1D-CNN-BiLSTM neural network model includes an input layer (input), a convolutional neural network layer (CNN), a BiLSTM layer, a Fully Connected layer (full Connected), and an output layer (output):
the CNN layer comprises convolutional layers (ConV) and a Pooling layer (Max Pooling), and the activation function of each convolutional layer is a Relu function;
the pooling method of the pooling layer is maximum pooling;
the activation function of the output layer is a Softmax function.
Further, the BilTM layer comprises a bidirectional LSTM layer, and the LSTM layer specifically comprises the following components:
ft＝σ(Wf[ht-1,xt]+bf)
it＝σ(Wi[ht-1,xt]+bi)
ot＝σ(Wo[ht-1,xt]+bo)
ct＝ftct-1+ittanh(Wc[ht-1,xt]+bc)
ht＝ottanhct
in the formula (f)tTo forget the door, itTo the input gate otTo the output gate, ctIs the cell state, σ is the sigmoid activation function, Wf、Wi、Wo、WcFor the weights in the training parameters, bf、bi、bo、bcAs a bias term in the training parameters, htFor hiding the state of the layer at the current moment, ht-1For hiding the state of the layer at the last moment, xtIs input at the current moment.
Further, the Relu function is:
Figure BDA0003451593720000031
where f (x) is the Relu function and x is the vector of the previous layer input.
Further, the Softmax activation function is:
Figure BDA0003451593720000041
in the formula, zi+1Is the value output from the i +1 th point in Z, C is the number of output nodes, ZcIs the value output from the c-th point in Z, and e is a natural constant.
Further, the interpretation layer is Grad-CAM + +.
Further, step S3 includes the steps of:
s31, calculating the weight of each category of each channel after the last convolution layer of the 1D-CNN-BilSTM neural network model:
Figure BDA0003451593720000042
in the formula, YxThe score of the category x is the degree of importance,
Figure BDA0003451593720000043
for the weight of the kth feature map for class x,
Figure BDA0003451593720000044
is the pixel value at (i, j) of the kth feature map,
Figure BDA0003451593720000045
(ii) a weight for category x at (i, j) of the kth feature map;
s32, generating a class activation graph according to the weight:
Figure BDA0003451593720000046
and S33, converting the size of the class activation map into the size of the original input image, and overlaying the class activation map on the original image through a thermodynamic map, wherein the size of the thermodynamic map corresponds to the activation degree of the 1D-CNN-BilSTM neural network model on the input signal.
Further, the specific step of step S4 is:
s41, connecting an explanation layer and a BilSTM layer after the 1D-CNN-BilSTM neural network model;
s42, taking the explanation layer as the middle layer of the whole model to explain the convolution process;
and S43, taking the BilSTM layer as the rear end of the whole model, taking the output of the last convolution layer of the CNN as the input of the rear end, and carrying out fault diagnosis.
Further, the specific step of step S5 is:
s51, classifying the samples collected in the step S1, wherein the four types are normal, outer ring fault, inner ring fault and rolling body fault;
s52, dividing the fault sample into a training set and a testing set, and performing training and testing by using a K-fold cross validation method;
and S53, performing model training by taking the training set as the input of the neural network model with the convolution interpretation capability to obtain the model with the fault diagnosis capability.
Compared with the prior art, the invention has the following advantages and effects:
(1) has interpretability and universality. In simulation analysis and experimental verification, Grad-CAM + + is used for proving the reasonability and the interpretability of the feature extraction of the convolutional neural network. The method can be applied to two-dimensional pictures, can also be applied to one-dimensional time signals such as rotary mechanical faults and the like, expands the application range of class activation mapping maps and has universality.
(2) The diagnosis precision and the classification result are good. The rolling bearing fault data set of the university of Keiss storage is adopted for carrying out experiments, and the diagnosis result is excellent for the clustering effect of the same category and the classifying effect of different categories.
(3) The noise resistance and robustness are good, the diagnosis precision of the model is higher, and when the noise is gradually reduced, the diagnosis precision is converged faster.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. The drawings constitute a part of this application and are intended as non-limiting examples embodying the inventive concept and not as limiting in any way.
FIG. 1 is a flow chart of an implementation of the method of the present invention;
FIG. 2 is a diagram of a neural network architecture in the method of the present invention;
FIG. 3a is Grad-CAM + + diagram of the outer ring fault of the rolling bearing experiment at 1797 r/min;
FIG. 3b is a Grad-CAM + + diagram of the failure of the experimental inner ring of the rolling bearing at 1797 r/min;
FIG. 3c is a Grad-CAM + + diagram of rolling element failure in a rolling bearing experiment at 1797 r/min;
FIG. 3d is Grad-CAM + + diagram of the rolling bearing in the normal state in the experiment at 1797 r/min;
FIG. 4 is a t-SNE analysis graph of the fault diagnosis result at 1797r/min in the method of the present invention;
FIG. 5 is a fault diagnosis result confusion matrix chart at 1797r/min in the method of the present invention;
FIG. 6a is Grad-CAM + + diagram of the failure of the experimental outer ring of the rolling bearing at 1772 r/min;
FIG. 6b is a Grad-CAM + + diagram of the failure of the experimental inner ring of the rolling bearing at 1772 r/min;
FIG. 6c is a Grad-CAM + + diagram of rolling element failure in a rolling bearing experiment at 1772 r/min;
FIG. 6d is Grad-CAM + + diagram of the rolling bearing in the normal state in the experiment at 1772 r/min;
FIG. 7 is a t-SNE analysis graph of the fault diagnosis results at 1772r/min in the method of the present invention;
FIG. 8 is a graph of a fault diagnosis result confusion matrix at 1772r/min in the method of the present invention;
FIG. 9a is a Grad-CAM + + diagram of the failure of the experimental outer ring of the rolling bearing at the rotation speed of 1750 r/min;
FIG. 9b is a Grad-CAM + + diagram of the failure of the experimental inner ring of the rolling bearing at the rotation speed of 1750 r/min;
FIG. 9c is a Grad-CAM + + diagram of rolling element failure in a rolling bearing experiment at 1750 r/min;
FIG. 9d is a Grad-CAM + + diagram of the rolling bearing in the normal state in the experiment at 1750 r/min;
FIG. 10 is a t-SNE analysis graph of the fault diagnosis result at 1750r/min in the method of the present invention;
FIG. 11 is a fault diagnosis result confusion matrix chart at 1750r/min in the method of the present invention;
FIG. 12 is a graph of diagnostic accuracy for various network models at different signal-to-noise ratios in the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The invention provides an interpretable rolling bearing fault diagnosis method, which is described in detail below.
As shown in fig. 1, a schematic flow chart of an embodiment of an interpretable rolling bearing fault diagnosis method based on Grad-CAM + + and CNN-BiLSTM according to an embodiment of the present invention includes the following steps:
s1, collecting a one-dimensional time sequence signal of the rolling bearing and expanding a sample;
s2, establishing an initial 1D-CNN-BilSTM neural network model;
s3, adding a Grad-CAM + + explanation layer to the neural network model;
s4, establishing a neural network model with convolution interpretation capability;
s5, training the neural network model by using various one-dimensional fault data to obtain a model with fault diagnosis capability;
the data are specifically bearing outer ring faults, inner ring faults, rolling body faults and normal data. Dividing the fault samples into a training set and a testing set, training and testing by using a K-fold cross validation method, taking K as 10, taking 9 parts of the K for training each time, testing the rest 1 part of the K, taking the training set as the input of a 1D-CNN-BilSTM neural network model, carrying out model training with the dimensionality of each sample being 2048 multiplied by 1, and obtaining a model with fault diagnosis capability.
And S6, carrying out fault diagnosis on the rolling bearing through the fault diagnosis model.
According to the interpretable rolling bearing fault diagnosis method based on Grad-CAM + + and CNN-BilSTM provided by the embodiment of the invention, the convolution result of the convolutional neural network is well explained in a visual thermodynamic diagram form based on the Grad-CAM + + algorithm, the reliability of the diagnosis result is improved by taking the advantage of CNN local feature extraction and the natural characteristic that the BilSTM can well deal with the non-linear time sequence, and the parameters during network training can be reduced, so that the training speed is improved. Further comparing the model with 1D-CNN, 1D-CNN-SVM and 1D-CNN-LSTM with the same structure and parameters as the model of the invention, the 1D-CNN-BiLSTM can be verified to explain the excellent noise resistance of the neural network.
In some embodiments of the present invention, as shown in fig. 1, S1 includes:
s11, collecting four types of one-dimensional time signals of normal, outer ring fault, inner ring fault and rolling body fault collected by the acceleration sensor, taking a Kaiser Sichu university rolling bearing fault data set as an example, carrying out three times of experiments at different rotating speeds, wherein the three times of experiments are 1797r/min, 1772r/min and 1750r/min respectively;
s12, expanding the one-dimensional time signal in an overlapped sampling mode, and dividing a data sample into a training set and a test set;
and S13, dividing the data sample into different training sets and test sets for multiple times in a K-fold cross validation mode, and finally averaging results to improve the robustness of the model.
In some embodiments of the present invention, as shown in fig. 2, the structure of the initial 1D-CNN-BiLSTM neural network model includes an input layer (input), two convolutional layers (ConV), two Pooling layers (Max power), a BiLSTM layer, a Fully Connected layer (full Connected), and an output layer (output):
a first convolutional layer of the convolutional layers has 16 convolutional kernels with dimensions of 4 x 1, a second convolutional kernel has 32 convolutional kernels with dimensions of 2 x 1, and the activation function of each convolutional layer is a Relu function;
specifically, the Relu function is:
Figure BDA0003451593720000091
where f (x) is the Relu function and x is the vector of the previous layer input.
The pooling method of the pooling layers is maximum pooling, the step length of the first pooling layer is 4, and the step length of the second pooling layer is 2;
the number of the neurons of the first full-connection layer is 64, the number of the neurons of the second full-connection layer is 4, and the dropout rejection rate is set to be 0.5, so that overfitting is prevented.
The number of the neurons of the output layer is consistent with the number of the fault types, and the activation function is a Softmax function.
Specifically, the Softmax function is:
Figure BDA0003451593720000092
in the formula, zi+1Is the value output from the i +1 th point in Z, C is the number of output nodes, ZcIs the value output from the c-th point in Z, and e is a natural constant.
Further, in some embodiments of the present invention, the adding a Grad-CAM + + interpretation layer to the neural network model to obtain a model with convolution interpretation capability is shown in fig. 3, and S3 includes:
s31, calculating the weight of each channel to each category after convolutional layer, as follows:
Figure BDA0003451593720000093
in the formula, YxThe score of the category x is the degree of importance,
Figure BDA0003451593720000094
for the weight of the kth feature map for class x,
Figure BDA0003451593720000095
is the pixel value at (i, j) of the kth feature map,
Figure BDA0003451593720000096
is the weight for the class x at (i, j) of the kth feature map.
S32, the calculation mode of the generated class activation graph is as follows:
Figure BDA0003451593720000097
and S33, converting the size of the class activation map into the size of the original input image, and overlaying the class activation map on the original image through a thermodynamic map, wherein the value of the thermodynamic map corresponds to the activation degree of the convolutional neural network at the position.
Three examples were chosen for illustration. Example 1 is the case when the rotational speed is 1797r/min, example 2 is the case when the rotational speed is 1772r/min, and example 3 is the case when the rotational speed is 1750r/min, and the three examples are analyzed together as follows.
As shown in FIGS. 3a, 6a and 9a, the 1D-CNN-BilSTM neural network has a larger activation degree to the impact part of the input signal, and the convolution layer of the neural network is responsible for extracting the fault characteristics in the experiment, so that the network has a larger weight to the impact part and pays more attention to the problem.
In the bearing with the inner ring failed, as shown in fig. 3b, fig. 6b and fig. 9b, the neural network has a larger activation degree for the impact part of the input signal similarly to the case of the outer ring failure, which indicates that the weight of the network for the impact part is larger.
In the bearing with a rolling element fault, as shown in fig. 3c, fig. 6c and fig. 9c, unlike the inner and outer ring fault, the time domain signal impact part of the rolling element fault sample is not uniformly distributed, and no impact area is particularly obvious, but compared with the signal of the normal bearing in fig. 3d, fig. 6d and fig. 9d, the signal is still selectively activated by the neural network, and the activation area is weighted more.
As shown in fig. 3d, fig. 6d, and fig. 9d, similarly, the active portions of the Grad-CAM + + diagram are distributed in each region, that is, the weights of the portions are similar, so that the state of the normal bearing can be indicated.
Specifically, the auxiliary visualization method is used for analyzing the fault diagnosis result in fig. 4, 7 and 10, the fault diagnosis accuracy rate exceeds 98.5%, and the superiority of the method provided by the invention can be obviously seen. Fig. 5, 8 and 11 are t-SNE dimension reduction visualizations, and taking fig. 5 as an example, in the category 3, there are individual samples classified into the category 2 and the category 4, and in the category 4, there are a small number of samples classified into the category 2, so the diagnosis result does not reach 100%. However, according to the results of dimension reduction visualization, the rolling bearing fault data set of the university of Kessi storage is adopted for experiments, and the diagnosis result and t-SNE analysis show that the method disclosed by the invention has excellent clustering effect on the same category and classification effect on different categories. The network of the invention can well cluster samples of the same category and can also well classify different samples.
Further, comparing the model with 1D-CNN, 1D-CNN-SVM and 1D-CNN-LSTM with the same structure and parameters as the model of the invention, the results are shown in FIGS. 6 and 12, which show that the diagnosis precision of the model of the invention is higher under the same noise condition, and when the noise is gradually reduced, the diagnosis precision converges faster, and the 1D-CNN-BiLSTM is verified to explain the excellent anti-noise property of the neural network.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (10)

1. An interpretable rolling bearing fault diagnosis method is characterized by comprising the following steps:
s1, collecting a one-dimensional time sequence signal of the rolling bearing and expanding a sample;
s2, establishing an initial 1D-CNN-BilSTM neural network model;
s3, adding an explanation layer to the neural network model;
s4, establishing a neural network model with convolution interpretation capability;
s5, training the neural network model by using various one-dimensional fault data to obtain a model with fault diagnosis capability;
and S6, carrying out fault diagnosis on the rolling bearing through the fault diagnosis model.
2. The method for diagnosing the failure of the interpretable rolling bearing according to claim 1, wherein the step S1 comprises the steps of:
s11, collecting normal, outer ring fault, inner ring fault and rolling body fault one-dimensional time signals collected by the acceleration sensor;
s12, expanding the one-dimensional time signal in an overlapped sampling mode, and dividing a data sample into a training set and a test set;
and S13, dividing the data sample into different training sets and test sets for multiple times in a K-fold cross validation mode, and finally averaging results to improve the robustness of the model.
3. The method of claim 1, wherein the initial 1D-CNN-BiLSTM neural network model has a structure comprising an input layer, a convolutional neural network layer (CNN), a BiLSTM layer, a fully connected layer, and an output layer:
the CNN layer comprises convolution layers and pooling layers, and the activation function of each convolution layer is a Relu function;
the pooling method of the pooling layer is maximum pooling;
the activation function of the output layer is a Softmax function.
4. The method of claim 3, wherein the BilSTM layer comprises a bidirectional LSTM layer, and the LSTM layer comprises:
ft＝σ(Wf[ht-1,xt]+bf)
it＝σ(Wi[ht-1,xt]+bi)
ot＝σ(Wo[ht-1,xt]+bo)
ct＝ftct-1+ittanh(Wc[ht-1,xt]+bc)
ht＝ottanhct
in the formula (f)tTo forget the door, itTo the input gate otTo the output gate, ctIs the cell state, σ is the sigmoid activation function, Wf、Wi、Wo、WcFor the weights in the training parameters, bf、bi、bo、bcAs a bias term in the training parameters, htFor hiding the state of the layer at the current moment, ht-1For hiding the state of the layer at the last moment, xtIs input at the current moment.
5. The method of claim 3, wherein the Relu function is:
Figure FDA0003451593710000021
where f (x) is the Relu function and x is the vector of the previous layer input.
6. The method of claim 3, wherein the Softmax activation function is:
Figure FDA0003451593710000022
in the formula, zi+1Is the value output from the i +1 th point in Z, C is the number of output nodes, ZcIs the value output from the c-th point in Z, and e is a natural constant.
7. The method of claim 1, wherein the interpretation layer is Grad-CAM + +.
8. The interpretable rolling bearing fault diagnosing method according to claim 1 or 7, wherein step S3 includes the steps of:
s31, calculating the weight of each category of each channel after the last convolution layer of the 1D-CNN-BilSTM neural network model:
Figure FDA0003451593710000031
in the formula, YxThe score of the category x is the degree of importance,
Figure FDA0003451593710000032
for the weight of the kth feature map for class x,
Figure FDA0003451593710000033
is the pixel value at (i, j) of the kth feature map,
Figure FDA0003451593710000034
(ii) a weight for category x at (i, j) of the kth feature map;
s32, generating a class activation graph according to the weight:
Figure FDA0003451593710000035
and S33, converting the size of the class activation map into the size of the original input image, and overlaying the class activation map on the original image through a thermodynamic map, wherein the size of the thermodynamic map corresponds to the activation degree of the 1D-CNN-BilSTM neural network model on the input signal.
9. The method for diagnosing the failure of the interpretable rolling bearing according to claim 8, wherein the specific steps of step S4 are as follows:
s41, connecting an explanation layer and a BilSTM layer after the 1D-CNN-BilSTM neural network model;
s42, taking the explanation layer as the middle layer of the whole model to explain the convolution process;
and S43, taking the BilSTM layer as the rear end of the whole model, taking the output of the last convolution layer of the CNN as the input of the rear end, and carrying out fault diagnosis.
10. The method for diagnosing the failure of the interpretable rolling bearing according to any one of claims 1 to 9, wherein the step S5 includes the steps of:
s51, classifying the samples collected in the step S1, wherein the four types are normal, outer ring fault, inner ring fault and rolling body fault;
s52, dividing the fault sample into a training set and a testing set, and performing training and testing by using a K-fold cross validation method;
and S53, performing model training by taking the training set as the input of the neural network model with the convolution interpretation capability to obtain the model with the fault diagnosis capability.
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