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| An Improved 1DCNN-GRU for Rolling Bearing Fault Diagnosis |
| JIN Hai-long,MA Wu-xu,MENG Zong,SUN Deng-yun,CAO Wei,FAN Feng-jie |
| College of Electrical Engineering,Yanshan University, Hebei, Qinhuangdao 066000, China |
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Abstract To solve the issue that most traditional models of rolling bearing fault diagnosis can not fully exploit the spatial and temporal characteristics of the signals and require lots of expert knowledge, a novel fault diagnosis method based on one-dimensional convolutional neural networks(1DCNN) and gated recurrent neural networks (GRU) was proposed. Firstly, the convolution layers with different convolution kernels were used to maximize the extraction of spatial features in the signal. Secondly, the IReLU was proposed to enhance the feature extraction ability of the network. Then, the stacked GRU was introduced to further extract the temporal features in the output data of the 1DCNN module and complete the fusion of the spatial and temporal features. Finally, the fused features were recognized. The experimental results showed that the fault recognition accuracy of the proposed method is up to 99.96%, and the proposed method has a high identification accuracy and strong generalization performance for datasets under different loads.
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Received: 15 July 2022
Published: 21 September 2023
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2023, Vol. 44 
