Fault diagnosis method for rolling bearings based on AMCNN-BiGRU

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (18) : 71-80.

XU Peng1，GAO Jun2，SHAO Xing2

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Abstract

In order to overcome the disadvantage of manual feature extraction in traditional rolling bearing fault diagnosis methods, a rolling bearing fault diagnosis method based on convolution neural network-bi-directional gated cycle unit (AMCNN-BiGRU) based on attention module is proposed. In this method, the original vibration signal after downsampling is used as input, features are extracted from the original data by parallel convolution blocks with two different core sizes, and the extracted features are weighted and fused by attention module. Finally, the features with different weights are input to the bi-directional gated cycle unit for fault classification, so as to realize end-to-end diagnosis. In order to understand the diagnosis process of the proposed model, the learned features are visualized, and it is found that the model can effectively map different faults. The experimental results show that the model can effectively shorten the network training time and maintain 100% diagnostic accuracy by using the downsampled original data.

Key words

convolutional neural network / gate recurrent unit / attention mechanism / bearing fault diagnosis / visualization

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XU Peng1，GAO Jun2，SHAO Xing2. Fault diagnosis method for rolling bearings based on AMCNN-BiGRU[J]. Journal of Vibration and Shock, 2023, 42(18): 71-80

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