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Few-shot RUL estimation based on model-agnostic meta-learning

Author

Listed:
  • Yu Mo

    (Tsinghua University
    Tsinghua Shenzhen International Graduate School)

  • Liang Li

    (Tsinghua University)

  • Biqing Huang

    (Tsinghua University)

  • Xiu Li

    (Tsinghua Shenzhen International Graduate School)

Abstract

Data-driven remaining useful life (RUL) estimation has been a research hotspot in the prognostic and health management (PHM) of industrial equipment and systems. It can achieve predictive maintenance of machinery and rarely require prior expertise in prognostics and signal processing. However, the data-driven methods require sufficient training data, which is difficult to acquire. In this paper, we employ the model-agnostic meta-learning (MAML) algorithm to seek suitable model parameter initialization that can rapidly adapt to the given test sample with few-shot training samples. We also propose to build pseudo-meta-RUL task sets for meta-learning by calculating time sequence similarities. To further improve the applicability of the model, we extend the proposed method from few-shot conditions to general conditions. We conduct experiments on the C-MAPSS dataset and the results show that the proposed algorithm can improve the prediction performance and enhance the generalization ability of the model in the context of few-shot conditions and general conditions.

Suggested Citation

  • Yu Mo & Liang Li & Biqing Huang & Xiu Li, 2023. "Few-shot RUL estimation based on model-agnostic meta-learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2359-2372, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01929-w
    DOI: 10.1007/s10845-022-01929-w
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    References listed on IDEAS

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    1. Qianhui Wu & Keqin Ding & Biqing Huang, 2020. "Approach for fault prognosis using recurrent neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1621-1633, October.
    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. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    4. 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.
    5. 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).
    6. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
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    Cited by:

    1. Yang, Jing & Wang, Xiaomin, 2024. "Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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