Rolling bearing fault diagnosis method based on residual neural network

Abstract

Abstract: Because the rolling bearing has been working for a long time in an environment with complex and changeable working conditions and large noise interference, the bearing fault diagnosis method has a poor diagnostic effect under variable working conditions, aiming at this problem, a method of rolling bearing fault diagnosis based on residual neural network is proposed. First, the time domain signal data of rolling bearing is used as the input. Because the signal has a strong time-varying nature, the data pooling layer is improved for this characteristic. The improved data pooling layer is constructed by using three consecutive convolution layers in series. The purpose is to effectively extract feature information of the vibration signal and reduce the calculation amount of residual neural network parameters. Then a kind of dilated residual block combined with dilated convolution and residual block is designed for feature information learning. Finally, the dropout layer is added after the full connection layer to discard a certain proportion of neurons, which can effectively avoid the negative effects of over fitting. The bearing data sets of Case Western Reserve University is used for simulation experiment, and compared with SVM+EMD+Hilbert envelope spectrum, BPNN+EMD+Hilbert envelope spectrum and Resnet, the results show that the proposed method has higher diagnosis accuracy, stronger noise resistance and generalization ability in the fault diagnosis of rolling bearing under variable working conditions.

Key words: residual neural network, fault diagnosis, rolling bearing, variable working conditions

CLC Number:

ZHU Qi-xian, LIANG Hao-peng, ZHAO Xiao-qiang, SONG Zhao-yang. Rolling bearing fault diagnosis method based on residual neural network[J]. Journal of Lanzhou University of Technology, 2022, 48(3): 86-93.

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