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A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks

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  • Zhang, Huixin
  • Xi, Xiaopeng
  • Pan, Rong

Abstract

Accurate remaining useful life (RUL) prediction is of great importance for predictive maintenance. With the recent advancements in sensor technology and artificial intelligence, the data-driven approaches to RUL prediction of industrial equipment have gained a lot of attention. However, past researches have not adequately considered the variety of degradation rates and the accumulated information in degradation processes. To deal with this problem, a novel two-stage machine learning approach of RUL prediction is proposed in this paper. A set of nonlinear health indicator functions are constructed to guide the training process of a long short-term memory learner of degradation processes, then a time delay neural network is utilized for RUL prediction. The superiority of the proposed approach in terms of prediction accuracy and conservativeness is demonstrated by a case study of rolling element bearing dataset.

Suggested Citation

  • Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002466
    DOI: 10.1016/j.ress.2023.109332
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    Cited by:

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    4. Li, Yajing & Wang, Zhijian & Li, Feng & Li, Yanfeng & Zhang, Xiaohong & Shi, Hui & Dong, Lei & Ren, Weibo, 2024. "An ensembled remaining useful life prediction method with data fusion and stage division," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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