CN114372492A - An interpretable fault diagnosis method for rolling bearings - Google Patents

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

An interpretable fault diagnosis method for rolling bearings

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 technique

Rolling bearings are widely used in various industries, such as vehicles, aerospace. The health of the rolling bearing is the basic guarantee for the smooth operation of the whole equipment, so it is of great significance to monitor and comprehensively evaluate the running state of the rolling bearing. Rolling bearings are prone to fatigue damage and performance degradation, and affect the safety and reliability of the entire system. Research on fault diagnosis and health management has important theoretical research significance and engineering application value.

There are two problems in the traditional fault diagnosis method. One is that it requires a lot of expert knowledge related to mechanical engineering and statistics. It is not ideal and does not have self-adaptation, so it is necessary to find a method based on feature learning for fault feature extraction and diagnosis research.

With the continuous development of artificial intelligence and deep learning, the convolutional neural network, with its shared convolution kernel, has no pressure on high-dimensional data processing, does not require too much prior knowledge, and automatically extracts features and other advantages, has been obtained in many fields. widely used. But most CNNs are not interpretable, resulting in the process often criticized as a "black box". In addition, the diagnostic performance of conventional CNNs is not satisfactory when the noise is large, so it is extremely important to propose an interpretable bearing fault diagnosis method with good noise immunity.

The deficiencies in the prior art bearing detection method based on convolutional neural network (CN201910985498.4) are as follows:

1. In the derivation process of Grad-CAM used, the weight is estimated by the size Z of the feature map, so the size of the weight has a great relationship with the size of the feature map, which will affect the result. In addition, the method of the present invention cannot activate the same class that appears multiple times in the input image.

2. Its anti-noise effect is unknown.

SUMMARY OF THE INVENTION

The purpose of the present invention is to propose an interpretable rolling bearing fault diagnosis method aiming at the deficiencies of the prior art. It aims to solve the technical problems of traditional fault diagnosis methods that require more prior knowledge, CNN process is often criticized as black box and poor noise resistance of ordinary CNN.

The present invention is realized by at least one of the following technical solutions.

An interpretable rolling bearing fault diagnosis method, comprising the following steps:

S1. Collect the one-dimensional time series signal of the rolling bearing and expand the sample;

S2. Establish an initial 1D-CNN-BiLSTM neural network model;

S3, adding an explanation layer to the neural network model;

S4. Establish a neural network model with convolution interpretation capability;

S5, using a variety of one-dimensional fault data to train the neural network model to obtain a model with fault diagnosis capability;

S6. Perform fault diagnosis on the rolling bearing by using the fault diagnosis model.

Further, step S1 includes the following steps:

S11. Collect four types of one-dimensional time signals of normal, outer ring fault, inner ring fault and rolling element fault collected by the acceleration sensor;

S12, the one-dimensional time signal signal is expanded by means of overlapping sampling, and the data samples are divided into a training set and a test set;

S13. Divide the data sample into different training sets and test sets multiple times by means of K-fold cross-validation, and finally average the 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 (Fully Connected) and an output layer (output):

The CNN layer includes a convolution layer (ConV) and a pooling layer (Max Pooling), 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.

Further, the BiLSTM layer includes a bidirectional LSTM layer, and the composition of the LSTM layer is specifically:

f _t =σ(W _f [h _t-1 ,x _t ]+b _f )

i _t =σ(W _i [h _t-1 ,x _t ]+b _i )

o _t =σ(W _o [h _t-1 ,x _t ]+b _o )

c _t =f _t c _t-1 +i _t tanh(W _c [h _t-1 ,x _t ]+b _c )

h _t =o _t tanhc _t

where f _t is the forgetting gate, it is the input gate, _{o t is the output gate, c t is the cell state, σ is the sigmoid activation function, and W f , Wi , W o , and W c are the} weights in the training parameters , b _f , b _i , b _o , b _c are the bias items in the training parameters, h _t is the state of the hidden layer at the current moment, h _t-1 is the state of the hidden layer at the previous moment, and x _t is the current moment’s state of the hidden layer. enter.

Further, the Relu function is:

In the formula, f(x) is the Relu function, and x is the input vector of the previous layer.

Further, the Softmax activation function is:

In the formula, z _i+1 is the output value of the i+1th point in Z, _C is the number of output nodes, zc is the output value of the cth point in Z, and e is a natural constant.

Further, the interpretation layer is Grad-CAM++.

Further, step S3 includes the following steps:

S31. Calculate the weights of each channel after the last convolutional layer of the 1D-CNN-BiLSTM neural network model:

为第k个特征图对于类别x的权重，

为第k个特征图的(i,j)处的像素值，

为第k个特征图的(i,j)处对于类别x的权重；In the formula, Y ^x is the score value of category x, that is, the importance degree,

is the weight of the k-th feature map for the category x,

is the pixel value at (i, j) of the k-th feature map,

is the weight for category x at (i, j) of the k-th feature map;

S32. Generate a class activation map according to the weight:

S33. Transform the size of the class activation map to the size of the original input image, and then overlay it on the original image through a heatmap. The size of the heatmap value corresponds to the activation degree of the 1D-CNN-BiLSTM neural network model to the input signal. .

Further, the specific steps of step S4 are:

S41. Connect the explanation layer and the BiLSTM layer after the 1D-CNN-BiLSTM neural network model;

S42. Use the interpretation layer as the middle layer of the entire model to explain the convolution process;

S43. Use the BiLSTM layer as the back end of the entire model, and use the output of the last convolutional layer of the CNN as the input of the back end to perform fault diagnosis.

Further, the specific steps of step S5 are:

S51, classify the samples collected in step S1 into four categories, namely normal, outer ring fault, inner ring fault and rolling element fault;

S52, dividing the fault sample into a training set and a test set, and using the K-fold cross-validation method for training and testing;

S53. Use the training set as an input of a neural network model with convolution interpretation capability, and perform model training to obtain a model with fault diagnosis capability.

Compared with the prior art, the present invention has the following advantages and effects:

(1) It is interpretable and universal. In simulation analysis and experimental verification, Grad-CAM++ is used to prove the rationality and interpretability of feature extraction of convolutional neural network. The method of the invention can not only be applied to two-dimensional pictures, but also can be applied to one-dimensional time signals such as rotating machinery faults, so as to expand the scope of application of the class activation map and has universality.

(2) The diagnostic accuracy and classification results are good. Using the rolling bearing fault data set of Case Western Reserve University to conduct experiments, the diagnosis results have excellent clustering effects for the same category and classification effects for different categories.

(3) Good anti-noise and robustness, the model of the present invention has higher diagnostic accuracy, and when the noise gradually decreases, its diagnostic accuracy converges faster.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. The accompanying drawings form a part of this application, but only serve as non-limiting examples embodying the inventive concept and are not intended to be limiting in any way.

Fig. 1 is the flow chart that the method of the present invention is implemented;

Fig. 2 is a neural network architecture diagram in the method of the present invention;

Figure 3a is the Grad-CAM++ diagram of the outer ring failure of the rolling bearing experiment at a rotational speed of 1797r/min;

Figure 3b is the Grad-CAM++ diagram of the inner ring failure of the rolling bearing experiment at a rotational speed of 1797r/min;

Figure 3c is the Grad-CAM++ diagram of the rolling element failure of the rolling bearing experiment at a rotational speed of 1797r/min;

Figure 3d is the Grad-CAM++ diagram of the normal state of the rolling bearing experiment at 1797r/min;

Fig. 4 is the fault diagnosis result t-SNE analysis diagram under the rotating speed of 1797r/min in the method of the present invention;

Fig. 5 is the confusion matrix diagram of the fault diagnosis result under the rotating speed of 1797r/min in the method of the present invention;

Figure 6a is the Grad-CAM++ diagram of the outer ring failure of the rolling bearing experiment at a rotational speed of 1772r/min;

Figure 6b is the Grad-CAM++ diagram of the inner ring failure of the rolling bearing experiment at a rotational speed of 1772r/min;

Figure 6c is the Grad-CAM++ diagram of the rolling element failure of the rolling bearing experiment at a rotational speed of 1772r/min;

Figure 6d is the Grad-CAM++ diagram of the normal state of the rolling bearing experiment at a rotational speed of 1772r/min;

Fig. 7 is the fault diagnosis result t-SNE analysis diagram under the rotating speed of 1772r/min in the method of the present invention;

Fig. 8 is the confusion matrix diagram of the fault diagnosis result under the rotating speed of 1772r/min in the method of the present invention;

Figure 9a is the Grad-CAM++ diagram of the outer ring failure of the rolling bearing experiment at a rotational speed of 1750r/min;

Figure 9b is the Grad-CAM++ diagram of the inner ring failure of the rolling bearing experiment at a rotational speed of 1750r/min;

Figure 9c is the Grad-CAM++ diagram of the rolling element failure of the rolling bearing experiment at a rotational speed of 1750 r/min;

Figure 9d is the Grad-CAM++ diagram of the normal state of the rolling bearing experiment at a rotational speed of 1750r/min;

Fig. 10 is the t-SNE analysis diagram of the fault diagnosis result under the rotating speed of 1750r/min in the method of the present invention;

Fig. 11 is the confusion matrix diagram of the fault diagnosis result under the rotating speed of 1750r/min in the method of the present invention;

Fig. 12 is a diagram showing the diagnostic accuracy of various network models under different signal-to-noise ratios in the method of the present invention.

Detailed ways

In order to make the purposes, 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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

The present invention provides an interpretable rolling bearing fault diagnosis method, which will be described in detail below.

As shown in FIG. 1 , a schematic flowchart of an embodiment of an interpretable rolling bearing fault diagnosis method based on Grad-CAM++ and CNN-BiLSTM provided by an embodiment of the present invention includes the following steps:

S1. Collect the one-dimensional time series signal of the rolling bearing and expand the sample;

S2. Establish an initial 1D-CNN-BiLSTM neural network model;

S3. Add a Grad-CAM++ interpretation layer to the neural network model;

S4. Establish a neural network model with convolution interpretation capability;

S5, using a variety of one-dimensional fault data to train the neural network model to obtain a model with fault diagnosis capability;

The data are specifically bearing outer ring failure, inner ring failure, rolling element failure and normal data. The fault samples are divided into a training set and a test set, and the K-fold cross-validation method is used for training and testing, taking K=10, taking 9 of them for training each time, and the remaining 1 for testing, using the training set as The input of 1D-CNN-BiLSTM neural network model, the dimension of each sample is 2048×1, and the model is trained to obtain a model with fault diagnosis ability.

S6. Perform fault diagnosis on the rolling bearing by using the fault diagnosis model.

The interpretable rolling bearing fault diagnosis method based on Grad-CAM++ and CNN-BiLSTM provided by the embodiment of the present invention, the convolution result of the convolutional neural network based on the Grad-CAM++ algorithm is well explained in the form of a visual heat map This shows that the advantages of CNN in local feature extraction and BiLSTM can well handle the natural characteristics of nonlinear time series, improve the reliability of diagnosis results, and reduce the parameters when training the network, thereby improving the training speed. It is further compared with 1D-CNN, 1D-CNN-SVM and 1D-CNN-LSTM with the same structure and parameters as the model described in the present invention, and it is verified that the described 1D-CNN-BiLSTM can explain the excellent resistance of neural network. noise.

In some embodiments of the present invention, as shown in FIG. 1 , S1 includes:

S11. Collect the one-dimensional time signals of four categories of normal, outer ring fault, inner ring fault and rolling element fault collected by the acceleration sensor. Here, taking the rolling bearing fault data set of Case Western Reserve University as an example, carry out three different rotation speeds. The experiments were 1797r/min, 1772r/min, 1750r/min;

S12, the one-dimensional time signal signal is expanded by means of overlapping sampling, and the data samples are divided into a training set and a test set;

S13. Divide the data sample into different training sets and test sets multiple times by means of K-fold cross-validation, and finally average the 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 layers of convolution layers (ConV), and two layers of pooling layers (Max Pooling), BiLSTM layer, fully connected layer (Fully Connected) and output layer (output):

The first convolutional layer of the convolutional layer has 16 convolution kernels of 4×1 dimension, the second convolutional kernel has 32 convolutional kernels of 2×1 dimension, and the activation function of each convolutional layer is Relu function;

Specifically, the Relu function is:

In the formula, f(x) is the Relu function, and x is the input vector of the previous layer.

The pooling method of the pooling layer is maximum pooling, the step size of the first pooling layer is 4, and the step size of the second pooling layer is 2;

The number of neurons in the first fully connected layer is 64, the number of neurons in the second fully connected layer is 4, and the dropout dropout rate is set to 0.5 to prevent overfitting.

The number of neurons in the output layer is consistent with the number of fault types, and the activation function is the Softmax function.

Specifically, the Softmax function is:

In the formula, z _i+1 is the output value of the i+1th point in Z, _C is the number of output nodes, zc is the output value of the cth point in Z, and e is a natural constant.

Further, in some embodiments of the present invention, the Grad-CAM++ interpretation layer is added to the neural network model to obtain a model with convolution interpretation capability. The result is shown in Figure 3, and S3 includes:

S31. Calculate the weights of each channel for each category after the convolutional layer, as shown below:

为第k个特征图对于类别x的权重，

为第k个特征图的(i,j)处的像素值，

为第k个特征图的(i,j)处对于类别x的权重。In the formula, Y ^x is the score value of category x, that is, the importance degree,

is the weight of the k-th feature map for the category x,

is the pixel value at (i, j) of the k-th feature map,

is the weight for category x at (i, j) of the k-th feature map.

S32. The calculation method of the generated class activation graph is as follows:

S33, the size of the class activation map needs to be transformed to the size of the original input image, and then overlaid on the original image through a heat map, the value of the heat map corresponds to the activation degree of the convolutional neural network at this position.

Three embodiments are selected for description. Example 1 is the situation when the rotating speed is equal to 1797r/min, Example 2 is the situation when the rotating speed is equal to 1772r/min, and Example 3 is the situation when the rotating speed is equal to 1750r/min. Now the three embodiments are analyzed together as follows.

The bearing with the fault of the outer ring of the rolling bearing is shown in Figure 3a, Figure 6a, and Figure 9a. The 1D-CNN-BiLSTM neural network has a greater degree of activation for the impact part of the input signal, and the convolution layer of the neural network is used in this experiment. It is responsible for the extraction of fault features in the network, so it shows that the network has a larger weight on the impact part and pays more attention to it.

The bearing with inner ring fault is shown in Figure 3b, Figure 6b, and Figure 9b. Similar to the case of the outer ring fault, the neural network also has a greater degree of activation for the impact part of the input signal, indicating the weight of the network on this part. bigger.

The bearing with rolling element fault is shown in Figure 3c, Figure 6c, and Figure 9c. Different from the inner and outer ring faults, the time-domain signal impact part of the rolling element fault samples is unevenly distributed, and there is no particularly obvious impact area, but the signal of the normal bearing is different. Compared with Figure 3d, Figure 6d, and Figure 9d, it still shows that the neural network has selectively activated such signals, and the activation area has a greater weight.

The time domain signals of normal bearings are evenly distributed, as shown in Figure 3d, Figure 6d, and Figure 9d. Similarly, the activation part of the Grad-CAM++ map is also scattered in each area, that is, the weights of each part are similar, so it can be shown that Normal bearing condition.

Specifically, by analyzing the fault diagnosis results of the auxiliary visualization method Fig. 4, Fig. 7 and Fig. 10, the fault diagnosis accuracy rate is all over 98.5%, and the superiority of the method of the present invention can be clearly seen. Figure 5, Figure 8 and Figure 11 are t-SNE dimensionality reduction visualization diagrams. Taking Figure 5 as an example, in category 3, some samples are classified in category 2 and category 4, and in category 4, a small number of samples are classified In category 2, the diagnostic result does not reach 100%. However, according to the results of dimensionality reduction visualization, it can be seen that the rolling bearing fault data set of Case Western Reserve University is used for experiments. The diagnosis results and t-SNE analysis show that the method of the present invention has clustering effects for the same category and classification effects for different categories. All performed excellently. The network of the present invention can cluster samples of the same category well, and can also classify different samples well.

Further, compare it with 1D-CNN, 1D-CNN-SVM and 1D-CNN-LSTM with the same structure and parameters as the model described in the present invention, the results are shown in Figure 6 and Figure 12, the results show that the same The diagnostic accuracy of the model of the present invention is higher under noise conditions, and when the noise is gradually reduced, the diagnostic accuracy converges faster, which verifies the excellent noise immunity of the 1D-CNN-BiLSTM interpretable neural network.

The above-disclosed preferred embodiments of the present invention are provided only to help illustrate the present invention. The preferred embodiments do not exhaust all the details, nor do they limit the invention to only the described embodiments. Obviously, many modifications and variations are possible in light of the content of this specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is to be limited only by the claims and their full scope and equivalents.

Claims (10)

1. A fault diagnosis method for an interpretable rolling bearing, comprising the following steps: S1. Collect the one-dimensional time series signal of the rolling bearing and expand the sample; S2. Establish an initial 1D-CNN-BiLSTM neural network model; S3, adding an explanation layer to the neural network model; S4. Establish a neural network model with convolution interpretation capability; S5, using a variety of one-dimensional fault data to train the neural network model to obtain a model with fault diagnosis capability; S6. Perform fault diagnosis on the rolling bearing by using the fault diagnosis model. 2. An interpretable rolling bearing fault diagnosis method according to claim 1, wherein step S1 comprises the following steps: S11. Collect four types of one-dimensional time signals of normal, outer ring fault, inner ring fault and rolling element fault collected by the acceleration sensor; S12, the one-dimensional time signal signal is expanded by means of overlapping sampling, and the data samples are divided into a training set and a test set; S13. Divide the data sample into different training sets and test sets multiple times by means of K-fold cross-validation, and finally average the results to improve the robustness of the model. 3. An interpretable rolling bearing fault diagnosis method according to claim 1, wherein the structure of the initial 1D-CNN-BiLSTM neural network model comprises an input layer, a convolutional neural network layer (CNN), a BiLSTM layer, fully connected layer and output layer: The CNN layer includes a convolution layer and a pooling layer, and the activation function of each convolution layer is the Relu function; The pooling method of the pooling layer is maximum pooling; The activation function of the output layer is a Softmax function. 4. An interpretable rolling bearing fault diagnosis method according to claim 3, wherein the BiLSTM layer comprises a bidirectional LSTM layer, and the composition of the LSTM layer is specifically: f _t =σ(W _f [h _t-1 ,x _t ]+b _f ) i _t =σ(W _i [h _t-1 ,x _t ]+b _i ) o _t =σ(W _o [h _t-1 ,x _t ]+b _o ) c _t =f _t c _t-1 +i _t tanh(W _c [h _t-1 ,x _t ]+b _c ) h _t =o _t tanhc _t where f _t is the forgetting gate, it is the input gate, _{o t is the output gate, c t is the cell state, σ is the sigmoid activation function, and W f , Wi , W o , and W c are the} weights in the training parameters , b _f , b _i , b _o , b _c are the bias items in the training parameters, h _t is the state of the hidden layer at the current moment, h _t-1 is the state of the hidden layer at the previous moment, and x _t is the current moment’s state of the hidden layer. enter. 5. A kind of interpretable rolling bearing fault diagnosis method according to claim 3, is characterized in that, described Relu function is:

In the formula, f(x) is the Relu function, and x is the input vector of the previous layer. 6. A kind of interpretable rolling bearing fault diagnosis method according to claim 3, is characterized in that, described Softmax activation function is:

In the formula, z _i+1 is the output value of the i+1th point in Z, _C is the number of output nodes, zc is the output value of the cth point in Z, and e is a natural constant. 7. An interpretable rolling bearing fault diagnosis method according to claim 1, wherein the interpretation layer is Grad-CAM++. 8. An interpretable rolling bearing fault diagnosis method according to claim 1 or 7, wherein step S3 comprises the following steps: S31. Calculate the weights of each channel after the last convolutional layer of the 1D-CNN-BiLSTM neural network model:

In the formula, Y ^x is the score value of category x, that is, the importance degree,

is the weight of the k-th feature map for the category x,

is the pixel value at (i, j) of the k-th feature map,

is the weight for category x at (i, j) of the k-th feature map; S32. Generate a class activation map according to the weight:

S33. Transform the size of the class activation map to the size of the original input image, and then overlay it on the original image through a heatmap. The size of the heatmap value corresponds to the activation degree of the 1D-CNN-BiLSTM neural network model to the input signal. . 9. The interpretable rolling bearing fault diagnosis method according to claim 8, wherein the specific steps of step S4 are: S41. Connect the explanation layer and the BiLSTM layer after the 1D-CNN-BiLSTM neural network model; S42. Use the interpretation layer as the middle layer of the entire model to explain the convolution process; S43. Use the BiLSTM layer as the back end of the entire model, and use the output of the last convolutional layer of the CNN as the input of the back end to perform fault diagnosis. 10. An interpretable rolling bearing fault diagnosis method according to any one of claims 1 to 9, wherein the specific steps of step S5 are: S51, classify the samples collected in step S1 into four categories, namely normal, outer ring fault, inner ring fault and rolling element fault; S52, dividing the fault sample into a training set and a test set, and using the K-fold cross-validation method for training and testing; S53. Use the training set as the input of the neural network model with convolution interpretation capability, and perform model training to obtain a model with fault diagnosis capability.

Images