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A novel sub-label learning mechanism for enhanced cross-domain fault diagnosis of rotating machinery

Author

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  • Deng, Minqiang
  • Deng, Aidong
  • Shi, Yaowei
  • Liu, Yang
  • Xu, Meng

Abstract

Deep Domain Adaptation (DDA), which transfers the knowledge learned in the source domain to the target domain, has made remarkable achievements in intelligent fault diagnosis. However, the existing DDA technology mainly focuses on eliminating cross-domain distribution discrepancies, while ignoring the exploration of intra-domain distribution characteristics, resulting in unsatisfactory performance in complex scenarios. To overcome this drawback, a novel sub-label learning mechanism (SLLM) is proposed in this paper, which exploits the structural connectivity of the original sample space to guide distribution alignment, thereby enhancing domain adaptability. Specifically, SLLM consists of two parts. First, the unsupervised target domain is annotated with sub-labels according to the probability distribution of the sample space, so that similar data can be recognized. Then, the intra-domain connectivity of the associated data is preserved during feature matching. In this way, samples belonging to the same category can be aggregated together in the feature space, and mismatches can be effectively alleviated. Extensive experiments on two datasets indicate that the proposed SLLM can significantly improve the domain adaptability of traditional DDA methods.

Suggested Citation

  • Deng, Minqiang & Deng, Aidong & Shi, Yaowei & Liu, Yang & Xu, Meng, 2022. "A novel sub-label learning mechanism for enhanced cross-domain fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002344
    DOI: 10.1016/j.ress.2022.108589
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    References listed on IDEAS

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    1. Wang, Xu & Shen, Changqing & Xia, Min & Wang, Dong & Zhu, Jun & Zhu, Zhongkui, 2020. "Multi-scale deep intra-class transfer learning for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. 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).
    3. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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    Cited by:

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    2. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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    6. Xia, Pengcheng & Huang, Yixiang & Tao, Zhiyu & Liu, Chengliang & Liu, Jie, 2023. "A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Zhao, Ke & Hu, Junchen & Shao, Haidong & Hu, Jiabei, 2023. "Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
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    10. Zhang, Qing & Tang, Lv & Xuan, Jianping & Shi, Tielin & Li, Rui, 2023. "An uncertainty relevance metric-based domain adaptation fault diagnosis method to overcome class relevance caused confusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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