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| A rolling bearing fault diagnosis method based on deep attention transfer learning at different rotations |
| CHEN Renxiang1，TANG Linlin1，HU Xiaolin2，YANG Lixia3，ZHAO Ling1 |
1. Chongqing Engineering Laboratory for Transportation Engineering Application Robot, Chongqing Jiaotong University, Chongqing 400074, China;
2. School of Business Administration, Chongqing University of Science and Technology, Chongqing 401331, China;
3. Chongqing Innovation Center of Industrial Big-Data Co., Ltd., Chongqing 400056, China |
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Abstract Considering that it is difficult to extract features of rolling bearings at different speed, and the data distribution is very different, which affects the diagnosis results, a rolling bearing fault diagnosis method based on deep attention transfer learning at different rotating speeds was proposed. Firstly, the time-frequency diagram of vibration signal is obtained by wavelet transform to show the time-frequency characteristics. Secondly, an attention convolutional neural network (ACNN) based on space and channel is built as a feature extractor to extract the key features of the rolling bearing to prevent feature loss. Then, the domain adaptation layer is added to the ACNN to complete the distribution adaptation of rolling bearing depth features at different speeds. Finally, the health status of the target data was identified through the softmax classification layer. The feasibility and effectiveness of this method are proved by analyzing the experimental data of rolling bearing simulations with different degrees of failure at different speeds.
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Received: 25 January 2021
Published: 28 June 2022
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LEI Chunli1,2, XIA Benfeng1,2, XUE Linlin1,2, JIAO Mengxuan1,2, ZHANG Huqiang1,2. Rolling bearing fault diagnosis method based on MTF-CNN[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(9): 151-158. |
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