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Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors

Author

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  • Zhuang, Jichao
  • Jia, Minping
  • Ding, Yifei
  • Ding, Peng

Abstract

Many data-driven models normally assume that the training and test data are independent and identically distributed to predict the remaining useful life (RUL) of industrial machines. However, different failure models caused by variable failure behaviors may lead to a domain shift. Meanwhile, conventional methods lack comprehensive attention to temporal information, resulting in a limitation. To handle the aforementioned challenges, a transferable cross-domain approach for RUL estimation is proposed. The hidden features are extracted adaptively by a temporal convolution network in which residual self-attention is able to fully consider the contextual degradation information. Furthermore, a new cross-domain adaption architecture with the contrastive loss and multi-kernel maximum mean discrepancy is designed to learn the domain invariant features. The effectiveness and superiority of the proposed method are proved by the case study on IEEE PHM challenge 2012 bearing dataset and the comparison with other methods.

Suggested Citation

  • Zhuang, Jichao & Jia, Minping & Ding, Yifei & Ding, Peng, 2021. "Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004592
    DOI: 10.1016/j.ress.2021.107946
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Zhuang, Jichao & Jia, Minping & Zhao, Xiaoli, 2022. "An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Zhuang, Jichao & Jia, Minping & Cao, Yudong & Zhao, Xiaoli, 2022. "Semi-supervised double attention guided assessment approach for remaining useful life of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Li, Wanxiang & Shang, Zhiwu & Gao, Maosheng & Qian, Shiqi & Feng, Zehua, 2022. "Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Ding, Peng & Zhao, Xiaoli & Shao, Haidong & Jia, Minping, 2023. "Machinery cross domain degradation prognostics considering compound domain shifts," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Ma, Yulin & Li, Lei & Yang, Jun, 2022. "Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    8. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    9. Dong, Shaojiang & Xiao, Jiafeng & Hu, Xiaolin & Fang, Nengwei & Liu, Lanhui & Yao, Jinbao, 2023. "Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

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