Bearing fault diagnosis based on convolution gated recurrent neural network

Kai Xu; Qingbo Yuan; Qingdong Zhang; Wenhui Zhang; Weigang Hou; Honglu Su

doi:10.1117/12.2641000

13 October 2022 Bearing fault diagnosis based on convolution gated recurrent neural network

Kai Xu, Qingbo Yuan, Qingdong Zhang, Wenhui Zhang, Weigang Hou, Honglu Su

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870L (2022) https://doi.org/10.1117/12.2641000
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

To research the problems of the rolling bearing fault diagnosis under strong noise, an enhanced convolution gated recurrent neural network was proposed, which is mainly composed of convolution neural network and gated recurrent neural network. In the proposed method, a convolution neural network is used to extract relevant features from vibration signals, and a gated recurrent neural network is used to further process relevant features to realize the diagnosis of bearing fault and its severity in complex scenes. In order to further enhance the ability of the network to deal with noise, a signal input method based on random sampling strategy is proposed, and the activation function in convolution network is improved, so as to enhance the bearing fault diagnosis ability of the network in complex scenes. The experiment on the Case Western Reserve University public data set proves that the proposed network framework could achieve the leading bearing fault diagnosis accuracy in complex scenarios such as high noise.

Citation Download Citation

Kai Xu, Qingbo Yuan, Qingdong Zhang, Wenhui Zhang, Weigang Hou, and Honglu Su "Bearing fault diagnosis based on convolution gated recurrent neural network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870L (13 October 2022); https://doi.org/10.1117/12.2641000

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