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Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions

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  • Xu, Yadong
  • Yan, Xiaoan
  • Sun, Beibei
  • Liu, Zheng

Abstract

Convolutional neural networks, with a powerful ability for feature representation, have made vast inroads into motor fault diagnosis. However, most of the existing CNN models cannot favorably handle the data generated in variable-speed scenarios. First, the continuous irregular fluctuation of the motor makes the time domain interval between two adjacent fault pulses change continuously. Secondly, due to the complex transmission path of the signal under unstable conditions, the noise distribution is complex. To address this problem, a global contextual residual convolutional neural network is proposed. The major novelties fall into three aspects. First, to make full use of the features from all intermediate layers and explore multiscale information, a new hierarchical structure is adopted in the CNN model. Second, since different features are of different importance for fault detection tasks, the global context module is explored to guide the model to pay more attention to global discriminant features. Third, the features learned by the network can either promote each other or contradict each other, so a multi-feature fusion layer is introduced to integrate these features adaptively. Case studies using the benchmark motor dataset and the industrial motor bearing dataset are performed to validate the superiority of the GC-ResCNN.

Suggested Citation

  • Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002599
    DOI: 10.1016/j.ress.2022.108618
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    1. Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Saeed, Umer & Jan, Sana Ullah & Lee, Young-Doo & Koo, Insoo, 2021. "Fault diagnosis based on extremely randomized trees in wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Zhang, Liangwei & Lin, Jing & Shao, Haidong & Zhang, Zhicong & Yan, Xiaohui & Long, Jianyu, 2021. "End-to-end unsupervised fault detection using a flow-based model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    8. Yan, Xiaoan & Liu, Ying & Xu, Yadong & Jia, Minping, 2021. "Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity," Renewable Energy, Elsevier, vol. 170(C), pages 724-748.
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