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Rolling Bearing Fault Diagnosis Method Based on Multiple Efficient Channel Attention Capsule Network

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Abstract

In the environment of strong noise, it is very difficult to extract bearing fault characteristics from vibration signals. To solve the problem, this paper proposes a fault diagnosis method based on Multiple Efficient Channel Attention Capsule Network (MECA-CapsNet). Due to diverse scales channel of attention mechanism, MECA-CapsNet can obtain multi-scale channels feature, enhance information interaction between different channels, and fuse key information of diverse scale receptive field. So, our model can effectively abstract the key information of bearing fault characters from noisy vibration signal. To verify the effectiveness of MECA-CapsNet, experiments are carried out on the bearing data set of CWRU. When the signal-to-noise ratio is from 4 dB to −4 dB, the accuracies of MECA-CapsNet are better than typical fault diagnosis methods. Then, T-SNE technology is used to visualize the features extraction process. The visualization result verifies that multiple ECA modules on different scales can effectively reduce noise interference and improve the accuracy of rolling bearing fault diagnosis.

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Acknowledgments

The financial support provided from the National Natural Science Foundation of China (11702091), Natural Science Foundation of Hunan Province (2021JJ30267, 2019JJ50156) and Project of Hunan Provincial Department of Education (19B187, HNKCSZ-2020-0316) are greatly appreciated by the authors.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Kang Wu, Jie Tao, Hewen Chen & Shilei Yin

  2. Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, 411201, China

    Kang Wu, Hewen Chen & Shilei Yin

  3. Key Laboratory of Mechanical Equipment Health Maintenance, Hunan University of Science and Technology, Xiangtan, 411201, China

    Dalian Yang

  4. University of Wollongong, Wollongong, NSW, 2522, Australia

    Chixin Xiao

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  1. Kang Wu

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  2. Jie Tao

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  3. Dalian Yang

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  4. Hewen Chen

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  5. Shilei Yin

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  6. Chixin Xiao

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Corresponding author

Correspondence toJie Tao.

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Wu, K., Tao, J., Yang, D., Chen, H., Yin, S., Xiao, C. (2022). Rolling Bearing Fault Diagnosis Method Based on Multiple Efficient Channel Attention Capsule Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_29

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