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A robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for rotating machinery fault diagnosis

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  • Lin, Yanzhuo
  • Wang, Yu
  • Zhang, Mingquan
  • Zhao, Ming

Abstract

Unsupervised domain adaptation (UDA), usually trained jointly with labeled source data and unlabeled target data, is widely used to address the problem of lack of labeled data for new operating conditions of rotating machinery. However, due to the expensive storage costs and growing concern about data privacy, source-domain data are often not available, leading to the inapplicability of UDA. How to perform domain adaptation in scenarios without access to the source data has become an urgent problem to be solved. To this end, we propose a robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for fault diagnosis. The method only requires the use of the lightweight source model and unlabeled target data, which provides a new possibility to deploy domain adaptation models on resource-limited devices with good protection of data privacy. Specifically, based on proposed channel-level and instance-level uncertainty measures, adaptive calibration of source-domain model knowledge and target-domain risk samples during domain transfer is performed to attenuate the effect of negative transfer. Then, entropy minimization and target-domain diversity loss are introduced to redistribute the target samples and realize domain adaptation. Extensive cross-domain diagnostic experiments on two datasets demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Lin, Yanzhuo & Wang, Yu & Zhang, Mingquan & Zhao, Ming, 2025. "A robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s095183202400588x
    DOI: 10.1016/j.ress.2024.110516
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    References listed on IDEAS

    as
    1. Lu, Biliang & Zhang, Yingjie & Liu, Zhaohua & Wei, Hualiang & Sun, Qingshuai, 2023. "A novel sample selection approach based universal unsupervised domain adaptation for fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    2. Tian, Jilun & Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Zhang, Xingwu & Zhao, Yu & Yu, Xiaolei & Ma, Rui & Wang, Chenxi & Chen, Xuefeng, 2023. "Weighted domain separation based open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. 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).
    6. 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).
    7. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
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