Abstract

Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition-pseudo-Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.

Highlights

  • Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important

  • In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network(CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed

  • Each kind of sample research compares the impact of thirteen data sharing schemes on the Transfer learning (TL) accuracy and efficiency, and the test accuracy and time index are introduced to evaluate the model. e results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition-pseudo-Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s

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Summary

Research Framework

Part 2 is the model training process, in which the CNN model is built and the model parameters are shared for the noised vibration image samples and TL. As a feedforward neural network, the CNN model is composed of convolution layer, pooling layer, and fully connected layer, in which the convolution layer is used to convolute the image to obtain the featured map. The parameters are input into the activation function, which increases the nonlinearity of the model, so that the model can be applied to the complex classification problems. In the whole network structure, the fully connected layer generally acts as a classifier, for example, the Softmax function, and the calculated result is the output of the whole CNN model, as shown in the following equations: zli y(i l−1)W(l) + b(l),. The designated training layer is retained to participate in the training process of noised samples to complete the parameter updating

Research Basis
IMFA Image Generation Method
EP Time-Frequency Analysis Method
Determination of Pretraining Model Parameters
Parameter-Based TL
G C1-C2-C3-F1
Findings
Conclusions
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