CN114441173A - Rolling bearing fault diagnosis method based on improved depth residual shrinkage network - Google Patents

Rolling bearing fault diagnosis method based on improved depth residual shrinkage network Download PDF

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CN114441173A
CN114441173A CN202111630326.9A CN202111630326A CN114441173A CN 114441173 A CN114441173 A CN 114441173A CN 202111630326 A CN202111630326 A CN 202111630326A CN 114441173 A CN114441173 A CN 114441173A
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邓艾东
卞文彬
刘洋
刘东川
赵敏
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Abstract

The invention relates to a rolling bearing fault diagnosis method based on an improved deep residual shrinkage network, which comprises the following steps: s1, acquiring an input vibration signal, adding Gaussian white noise into the input vibration signal, repeatedly sampling the noise-added signal at intervals to obtain a sampling signal, matrixing the sampling signal, converting the sampling signal into a gray map sample, and endowing the sampling signal with a label of a bearing state; s2, splitting the gray-scale image sample into a training set and a test set in proportion; s3, adding the dense connection into an improved residual shrinkage module to construct an improved depth residual shrinkage network; s4, inputting the training set into an improved depth residual shrinkage network for training, obtaining errors through forward propagation, and optimizing parameters through backward propagation until the errors are converged; and S5, inputting the test set into the trained improved depth residual shrinkage network to obtain a fault classification result. The invention has faster convergence speed and stronger anti-noise interference capability, and improves the accuracy of fault diagnosis of the rolling bearing under strong noise variable working conditions.

Description

Rolling bearing fault diagnosis method based on improved depth residual shrinkage network
Technical Field
The invention relates to the technical field of fault diagnosis methods, in particular to a rolling bearing fault diagnosis method based on an improved deep residual shrinkage network.
Background
The rolling bearing is the most prone to failure in the gearbox of the wind turbine, and the failure of the rolling bearing can cause catastrophic damage to the gearbox, so that the wind turbine is stopped. Due to the fact that the operation condition of the rolling bearing is complex, vibration signals are weak during early failure and are easily submerged by strong interference signals, and the traditional method is very difficult to extract features. The current fault diagnosis methods are mainly divided into two types: methods based on vibration analysis and methods based on data driving. The vibration analysis comprises time frequency analysis, waveform analysis, correlation analysis, envelope spectrum analysis and the like. Vibration analysis requires a large amount of signal processing knowledge, the workload of manual feature extraction is large, and fault identification depends on manual experience, so that the generalization capability of the model is influenced. To avoid the limitations of vibration analysis, data-driven based methods are gradually developed and applied. Machine learning methods such as Support Vector Machines (SVMs), Random Forests (RFs), Artificial Neural Networks (ANNs), etc. are used for fault diagnosis. The traditional machine learning method extracts statistical parameters which are not enough to distinguish faults in many times, and the search for distinguishable feature integration is a long-term challenge.
In the prior art, a deep learning method has great potential in the field of bearing fault diagnosis. Although the deep learning method is improved in accuracy and generalization ability compared with manual feature extraction or the traditional machine learning method, the deep learning method still has the problem of low feature extraction discrimination under the conditions of complex working conditions and strong noise.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a rolling bearing fault diagnosis method based on an improved deep residual shrinkage network, which automatically extracts and identifies the fault characteristics of a rolling bearing under different working conditions containing strong noise by combining a dense connection module and the deep residual shrinkage network so as to realize self-adaptive characteristic extraction and fault diagnosis of the rolling bearing under the working conditions of strong noise variation.
The technical scheme adopted by the invention is as follows:
a rolling bearing fault diagnosis method based on an improved depth residual shrinkage network comprises the following steps:
s1, acquiring an input vibration signal, adding Gaussian white noise into the input vibration signal to obtain a noise-added signal, repeatedly sampling the noise-added signal at intervals to obtain a sampling signal, matrixing the sampling signal, converting the sampling signal into a gray-scale image sample, and giving a label of a bearing state to the sampling signal;
s2, splitting the gray scale image sample into a training set and a test set in proportion;
s3, constructing an improved depth residual shrinkage network:
the improved depth residual shrinkage network comprises a convolution layer and an improved residual shrinkage module which are sequentially stacked, and a global average pooling layer and an activation function for fault classification are arranged behind the last improved residual shrinkage module;
the improved residual shrinkage module comprises two bottleneck layers and a convolution layer for reducing dimension, wherein the two bottleneck layers and the convolution layer are sequentially arranged, and dense connection y is added between the adjacent layers, wherein the dense connection y is equal to F ([ x ═ x [)0，x1，x2，...，xl-1]) (ii) a The dense connection is used for fusing the input and output features of the previous layer as the input features of the next layer, wherein xl-1Representing the l-1 level output characteristic diagram, y representing the l level output characteristic diagram, and F (-) representing the characteristic diagram merging function;
s4, inputting the training set into the improved depth residual error shrinkage network for training, obtaining errors through forward propagation, and optimizing parameters through backward propagation until the errors are converged;
and S5, inputting the test set into the trained improved depth residual shrinkage network to obtain a fault classification result.
The further technical scheme is as follows:
the bottleneck layer consists of a 1 × 1 convolution layer and a 3 × 3 convolution layer, wherein the 1 × 1 convolution layer is positioned at the input side of the bottleneck layer and used for reducing dimension after dense connection; the 1 x 1 convolutional layers and the 3 x 3 convolutional layers each contain batch normalization and modified linear cell activation functions.
An attention module and a soft threshold function are arranged on the output side of the convolution layer for reducing the dimension, and an identical connection is arranged on the outermost layer of the improved residual error contraction module.
The improved depth residual error shrinkage network comprises three improved residual error shrinkage modules and three convolution layers with different sizes, and the maximum pooling layer is added behind the convolution layers of the two last layers respectively.
The three different sized convolutional layers are sequentially a 7 × 7 convolutional layer, a 5 × 5 convolutional layer and a 3 × 3 convolutional layer.
The error is a cross entropy loss function:
Figure BDA0003440387650000021
where E is the cross entropy error of the observed value, N is the number of fault classes, tjActual probability of an observation belonging to the jth class; y isjA probability of being predicted as class j for the observation,
Figure BDA0003440387650000022
xjinputting a feature map; x is the number ofcFor the c-th input feature map, Softmax (·) is an activation function.
The invention has the following beneficial effects:
dense connections are added to a deep residual shrinkage network, so that shallow and deep features are fully utilized, feature transfer is enhanced, and information flow is optimized. Compared with the network before improvement and other traditional machine learning models, the method has higher diagnosis accuracy, faster convergence speed and stronger anti-noise interference capability. The accuracy of fault diagnosis of the rolling bearing under the condition of strong noise and variable working conditions is greatly improved.
The requirement of automatically extracting and identifying the fault characteristics of the rolling bearing under the strong noise variable working condition is met on the premise of not manually extracting the characteristics, and end-to-end diagnosis is realized.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a block diagram of the residual shrinkage module of the present invention.
Fig. 3 is a diagram of the improved residual shrinkage module of the present invention.
Fig. 4 is a diagram of the improved depth residual shrinking network structure of the present invention.
FIG. 5 is a graph of diagnostic accuracy and confusion matrix with no noise added and 4 levels of noise added for an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
Referring to fig. 1, the present application relates to a rolling bearing fault diagnosis method based on an improved deep residual shrinkage network, which includes the following steps:
s1, acquiring an input vibration signal, adding Gaussian white noise into the input vibration signal to obtain a noise-added signal, repeatedly sampling the noise-added signal at intervals to obtain a sampling signal, matrixing the sampling signal, converting the sampling signal into a gray-scale image sample, and endowing the sampling signal with a bearing state label;
specifically, original vibration signals of the rolling bearing under different working conditions and in a normal state are collected, and data are cleaned through abnormal value and missing value processing to remove obvious abnormal vibration curves or missing parts, so that input vibration signals are obtained.
Wherein the fault types include: inner race failure, ball failure, and outer race failure.
S2, splitting the gray-scale image sample into a training set and a test set in proportion;
s3, constructing an improved depth residual shrinkage network:
the improved depth residual shrinkage network comprises a convolution layer and an improved residual shrinkage module which are sequentially stacked, and a global average pooling layer (GAP) and an activation function (Softmax) for fault classification are arranged after the last improved residual shrinkage module;
as shown in fig. 3, the improved residual shrinkage module includes two bottleneck layers and a convolution layer for reducing dimension, which are sequentially arranged, and dense connection y ═ F ([ x ] is added between adjacent layers0，x1，x2，...，xl-1]) (ii) a Dense connections are used to fuse previous layer input and output features as input features for the next layer, where xl-1Denotes the first-1 layer output characteristic diagram, y represents the l-th layer output characteristic diagram, and F (-) represents the characteristic diagram merging function;
as shown in fig. 3, the bottleneck layer is composed of a 1 × 1 convolutional layer and a 3 × 3 convolutional layer, the 1 × 1 convolutional layer is located at the input side of the bottleneck layer for reducing dimensions after dense connection; both the 1 × 1 convolutional layer and the 3 × 3 convolutional layer contain batch normalization and modified linear cell activation functions. "Concat connections", i.e. dense connections, are added between two bottleneck layers and between the following bottleneck layer and the last dimension-reducing convolutional layer, and the arrows of the curves indicate the calculation direction of the dense connections.
Specifically, the convolutional layer for dimension reduction is a 1 × 1 convolutional layer including a Batch Normalization (BN) and a modified linear unit activation function (ReLU).
The improved residual error shrinking module can be regarded as obtained by improving the structure of the conventional residual error shrinking module shown in fig. 2, namely, replacing two 3 × 3 convolution hidden layers in the structure of the conventional residual error shrinking module with two bottleneck layers and adding dense connection.
Wherein the dense connection theory is derived from a dense connection module Denseblock in a DenseNet algorithm.
Since dense connections drastically increase the number of feature maps, there is a problem that the computation amount is large although there is an advantage of fusing the features of the respective channels. Therefore, the method reduces the number of feature maps by performing parameter compression data dimension reduction after dense connection by using the 1 × 1 convolution layer in the bottleneck layer, thereby reducing the calculation amount.
Specifically, the convolution layer for dimension reduction of the improved residual error shrinkage module adopts a 1 × 1 convolution layer, the output side of the improved residual error shrinkage module is provided with an attention module and a soft threshold function, and the outermost layer of the improved residual error shrinkage module is provided with identity connection. Different attention is exerted to different channels through the attention module, noise is reasonably eliminated through a soft threshold function, and errors can conveniently flow back to the front layer through the equal connection.
As shown in fig. 4, the improved depth residual shrinking network structure of the present application includes three improved residual shrinking modules and three convolutional layers with different sizes.
Alternatively, three convolution layers of different sizes may be set to 7 × 7/5 × 5/3 × 3, 5 × 5/5 × 5/5 × 5, 5 × 5/5 × 5/3 × 3, 5 × 5/3 × 3/3 × 3, or 3 × 3/3 × 3/3 × 3, etc., and in view of extracting multi-dimensional features using convolution cores of different sizes, the present application preferably: the three different sized convolutional layers are sequentially a 7 × 7 convolutional layer, a 5 × 5 convolutional layer and a 3 × 3 convolutional layer, and the largest pooling layer is added after the last two convolutional layers (the 5 × 5 convolutional layer and the 3 × 3 convolutional layer).
S4, inputting the training set into an improved depth residual shrinkage network for training, obtaining errors through forward propagation, and optimizing parameters through backward propagation until the errors are converged;
wherein, the error adopts a cross entropy loss function:
Figure BDA0003440387650000041
where E is the cross entropy error of the observed value, N is the number of fault classes, tjActual probability of an observation belonging to the jth class; y isjA probability of being predicted as class j for the observation,
Figure BDA0003440387650000042
xjinputting a feature map; x is the number ofcFor the c input feature map, Softmax (-) is an activation function;
and S5, inputting the test set into the trained improved deep residual shrinkage network (DB-DRSN in the figure 1) to obtain a fault classification result.
As will be understood by those skilled in the art, C, W, H in fig. 2-4 represents the number of channels, width, and height of the feature map, respectively. Conv stands for convolutional layer, "Identity short" stands for Identity mapping (concatenation), BN stands for batch normalization, ReLU stands for modified linear unit activation function, Abs stands for absolute value, GAP stands for global average pooling, FC stands for fully concatenated layer, Maxpooling stands for maximum pooling, and Sigmoid and Softmax are both activation functions.
The technical solution of the present application is further described below with specific examples.
S1, obtaining an input vibration signal:
and acquiring original vibration signals of the rolling bearing at different rotating speeds and different loads through the public data set. The rolling bearing comprises a drive end bearing and a fan end bearing. Sampling frequencies include 12Khz and 48Khz, and four working conditions are collected: the fault types comprise fault states (inner ring fault, ball fault and outer ring fault) and vibration signals under normal state of 1797rpm/0Hp, 1772rpm/1Hp, 1750rpm/2Hp and 1730rpm/3Hp, wherein the outer ring fault is subdivided into outer ring 3 o ' clock direction fault, 6 o ' clock direction fault and 12 o ' clock direction fault, and the fault types comprise three fault diameters: 0.007inch, 0.014inch and 0.021 inch.
Through image observation of the original vibration signals, the vibration signals in the outer ring 3 o 'clock direction and the vibration signals in the outer ring 12 o' clock direction are found to have data loss after 120000 data points, and other fault states all contain over 240000 data points, so that the vibration signals of the first 120000 data points are uniformly selected as data samples. And cleaning the data by processing the abnormal value and the missing value, and removing an obvious abnormal vibration curve or a missing part to obtain an input vibration signal.
And adding noise to the input vibration signal, wherein the noise addition sets four noise levels from weak to strong, and Gaussian white noise with different variances and an average value of 0 is added to the input vibration signal. The noise level settings are shown in table 1. And then, repeatedly sampling at intervals and matrixing, intercepting every 400 data points as a sample, converting the sample into a 20 x 20 gray scale image after matrixing to serve as an input sample, and giving a sample data label according to the state of the rolling bearing.
TABLE 1
Figure BDA0003440387650000043
And S2, splitting the gray-scale image sample obtained in the step S1 into a training set and a test set according to a certain proportion. A gray map sample is intercepted according to each 400 points, each bearing state has 300 gray map samples, 200 of the samples are divided into training set samples, the remaining 100 samples are used as test set samples, and the specific conditions of the samples are shown in Table 2.
TABLE 2
Figure BDA0003440387650000044
Figure BDA0003440387650000051
And step S3, constructing an improved depth residual shrinkage network, and taking a 20 × 20 × 1 gray scale image sample constructed by the one-dimensional vibration signal as input. The network configuration parameter setting is shown in table 3, and the specific structure is shown in fig. 4.
TABLE 3
Figure BDA0003440387650000052
And step S4, inputting the training set sample into the constructed improved depth residual shrinkage network for training. Taking the cross entropy error function as a loss function of the sample data set, adopting an Adam optimizer, enabling the learning rate to be 0.001, the number of training iteration rounds to be 20, enabling the size of the mini-batch to be 50, utilizing a back propagation algorithm to iteratively update parameters of the improved depth residual shrinkage network, and when the loss function result of the sample data set tends to 0, indicating that the updated parameters of the improved depth residual shrinkage network tend to be stable, indicating that the rolling bearing fault diagnosis model based on the improved depth residual shrinkage network is trained completely, stopping the iterative updating process, and entering step S5.
And step S5, inputting the test set sample into the trained improved deep residual shrinkage network to obtain a fault state classification result and test diagnosis accuracy.
The diagnostic accuracy and confusion matrix for the improved depth residual shrinkage network with no noise added and 4 levels of noise added is shown in fig. 5. In the figure, Predictedlabel is a prediction label, Truelabel is an actual corresponding label, accuracy is a test accuracy, and misclass is a misclass rate. It can be seen from the figure that the diagnosis accuracy of the improved depth residual shrinkage network is 100% under the condition that no noise is added, and the noise levels I and II are respectively 99.33% and 99.5% in the noise levels III and IV, and the average diagnosis accuracy reaches 99.77% in 5 noise environments, which indicates that the improved depth residual shrinkage network has better effects on both the diagnosis accuracy and the anti-noise performance.
In order to verify the diagnosis performance of the rolling bearing fault diagnosis model based on the improved deep residual shrinkage network (named DB-DRSN), six models are selected for comparison, wherein the six models comprise the deep residual shrinkage network DRSN before improvement, the deep residual network DRN, other deep neural networks CNN and LeNet, and a traditional machine learning method SVM and ANN.
The DRSN, DRN network and parameter settings are completely identical to the DB-DRSN of the present application except for the improved structure. The settings of CNN and LeNet on the number of convolution hidden layers, learning rate and mini-batch are consistent with DB-DRSN. The SVM kernel function selects an RBF kernel function, and the penalty parameter is set to be 1. The ANN is consistent with DB-DRSN on the network layer number while adjusting the number of training rounds to 150 for error convergence. The bearing fault (sampling frequency is 48KHz, fault size is 0.007inch, and motor load is 0HP) diagnosis test accuracy of each model at SKF-6205 driving end of Kaiser university is shown in Table 4, and the data unit in the table is%.
TABLE 4
Figure BDA0003440387650000061
As can be seen from the table, with the increase of the noise level, the average diagnosis accuracy of the improved deep residual shrinkage network DB-DRSN of the application is higher than that of a DRSN model before improvement by 1.44%, and is higher than that of DRN by 3.76%, and compared with other deep neural networks and traditional models, the improvement is 3.33% -18.85%. The technical effect of the improved DB-DRSN on improving the accuracy of the strong noise fault diagnosis is verified.
In order to specifically analyze the diagnostic performance of the rolling bearing fault diagnosis model based on the improved deep residual shrinkage network under different working conditions, 4 different working conditions are set, which are respectively as follows: working condition 1: the motor load is 0HP, and the rotating speed is 1797 r/min; working condition 2: the motor load is 1HP, and the rotating speed is 1772 r/min; working condition 3: the load of the motor is 2HP, and the rotating speed is 1750 r/min; working condition 4: the motor load is 3HP, and the rotating speed is 1730 r/min. The definitions of data preprocessing, hyper-parameter setting, and noise level are all consistent with the above. The test accuracy of DB-DRSN under 4 working conditions is shown in Table 5, and the data unit in the table is%.
TABLE 5
Figure BDA0003440387650000062
As can be seen from the table, as the noise level increases, the average diagnostic accuracy of DB-DRSN under 4 working conditions gradually decreases, but still is higher than 97.2%, and the lowest diagnostic accuracy under different noise intensities and different working conditions is 95%. The method has good diagnosis effect under variable working conditions.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.

Claims (6)

1. A rolling bearing fault diagnosis method based on an improved deep residual shrinkage network is characterized by comprising the following steps:
s1, acquiring an input vibration signal, adding Gaussian white noise into the input vibration signal to obtain a noise-added signal, repeatedly sampling the noise-added signal at intervals to obtain a sampling signal, matrixing the sampling signal, converting the sampling signal into a gray-scale image sample, and giving a label of a bearing state to the sampling signal;
s2, splitting the gray scale image sample into a training set and a test set in proportion;
s3, constructing an improved depth residual shrinkage network:
the improved depth residual shrinkage network comprises a convolution layer and an improved residual shrinkage module which are sequentially stacked, and a global average pooling layer and an activation function for fault classification are arranged behind the last improved residual shrinkage module;
the improved residual shrinkage module comprises two bottleneck layers and a convolution layer for reducing dimension, wherein the two bottleneck layers and the convolution layer are sequentially arranged, and dense connection y is added between the adjacent layers, wherein the dense connection y is equal to F ([ x ═ x [)0，x1，x2，...，xl-1]) (ii) a The dense connection is used for fusing the input and output features of the previous layer as the input features of the next layer, wherein xl-1Representing the l-1 level output characteristic diagram, y representing the l level output characteristic diagram, and F (-) representing the characteristic diagram merging function;
s4, inputting the training set into the improved depth residual error shrinkage network for training, obtaining errors through forward propagation, and optimizing parameters through backward propagation until the errors are converged;
and S5, inputting the test set into a trained improved depth residual shrinkage network to obtain a fault classification result.
2. The method of claim 1, wherein the bottleneck layer is comprised of a 1 x 1 convolutional layer and a 3 x 3 convolutional layer, the 1 x 1 convolutional layer being located on the input side of the bottleneck layer for reducing dimensions after dense connection; the 1 x 1 convolutional layers and the 3 x 3 convolutional layers each contain batch normalization and modified linear cell activation functions.
3. The method of claim 1, wherein an attention module and a soft threshold function are provided at an output side of the convolutional layer for dimension reduction, and an identity connection is provided at an outermost layer of the improved residual puncturing module.
4. The method of claim 1, wherein the improved depth residual shrinking network comprises three improved residual shrinking modules and three convolutional layers with different sizes, and the convolutional layers of the last two layers are respectively added with a largest pooling layer.
5. The method of claim 4, wherein the three different sized convolutional layers are sequentially a 7 x 7 convolutional layer, a 5 x 5 convolutional layer, and a 3 x 3 convolutional layer.
6. The method according to claim 1, wherein in step S4, the error is a cross entropy loss function:
Figure FDA0003440387640000011
where E is the cross entropy error of the observed value, N is the number of fault classes, tjActual probability of an observation belonging to the jth class; y isjA probability of being predicted as class j for the observation,
Figure FDA0003440387640000012
xjinputting a feature map; x is a radical of a fluorine atomcFor the c-th input feature map, Softmax (·) is an activation function.
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