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Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions

Author

Listed:
  • Han Cheng

    (Xidian University)

  • Xianguang Kong

    (Xidian University)

  • Qibin Wang

    (Xidian University)

  • Hongbo Ma

    (Xidian University)

  • Shengkang Yang

    (Xidian University)

  • Gaige Chen

    (Xidian University)

Abstract

Remaining useful life (RUL) prediction can effectively avoid unexpected mechanical breakdowns, thus improving operational reliability. However, the distribution discrepancy caused by different working conditions may lead to deterioration in the prognostic task of machinery. Inspired by the idea of transfer learning, a novel intelligent approach based on dynamic domain adaptation (DDA) is proposed for the machinery RUL prediction of multiple working conditions in this paper. At first, reverse validation technology is utilized to select appropriate source samples to construct the training dataset. Then two dynamic domain adaptation networks are trained to extract domain invariant degradation feature and predict RUL, namely dynamic distribution adaptation network and dynamic adversarial adaptation network. In the dynamic domain adaptation network, the fuzzy set theory is employed to calculate conditional distribution discrepancy loss, and the dynamic adaptive factor is introduced to dynamically adjust the distribution weights. Finally, the proposed method is proved to be effective through two run-to-failure bearing datasets. Related experimental results indicate that, compared with other related RUL prediction methods, the DDA-based prognostic method not only achieves better prediction performance, but also avoids the influence of negative transfer and distribution weight fluctuation.

Suggested Citation

  • Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01814-y
    DOI: 10.1007/s10845-021-01814-y
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    References listed on IDEAS

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    1. Pradeep Kundu & Seema Chopra & Bhupesh K. Lad, 2019. "Multiple failure behaviors identification and remaining useful life prediction of ball bearings," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1795-1807, April.
    2. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    3. Lixiao Cao & Zheng Qian & Hamid Zareipour & David Wood & Ehsan Mollasalehi & Shuangshu Tian & Yan Pei, 2018. "Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions," Energies, MDPI, vol. 11(12), pages 1-20, November.
    4. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    5. Yu Mo & Qianhui Wu & Xiu Li & Biqing Huang, 2021. "Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1997-2006, October.
    6. Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
    7. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    8. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    9. Jinjiang Wang & Robert X. Gao & Zhuang Yuan & Zhaoyan Fan & Laibin Zhang, 2019. "A joint particle filter and expectation maximization approach to machine condition prognosis," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 605-621, February.
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    Citations

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    Cited by:

    1. Yi Lyu & Zhenfei Wen & Aiguo Chen, 2025. "A novel transfer learning approach based on deep degradation feature adaptive alignment for remaining useful life prediction with multi-condition data," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 619-637, January.
    2. Li, Jimeng & Mao, Weilin & Yang, Bixin & Meng, Zong & Tong, Kai & Yu, Shancheng, 2024. "RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Han, Yaoyao & Ding, Xiaoxi & Gu, Fengshou & Chen, Xiaohui & Xu, Minmin, 2025. "Dual-drive RUL prediction of gear transmission systems based on dynamic model and unsupervised domain adaption under zero sample," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    4. Chao Huang & Siqi Bu & Hiu Hung Lee & Kwong Wah Chan & Winco K. C. Yung, 2024. "Prognostics and health management for induction machines: a comprehensive review," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 937-962, March.
    5. Jiaxian Chen & Dongpeng Li & Ruyi Huang & Zhuyun Chen & Weihua Li, 2025. "A transfer regression network-based adaptive calibration method for remaining useful life prediction considering individual discrepancies in the degradation process of machinery," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2767-2783, April.
    6. Pei Wang & Tao Wang & Sheng Yang & Han Cheng & Pengde Huang & Qianle Zhang, 2024. "Production quality prediction of cross-specification products using dynamic deep transfer learning network," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2567-2592, August.

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