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

Huixin Zhang, Xiaopeng Xi and Rong Pan

Reliability Engineering and System Safety, 2023, vol. 237, issue C

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.

Keywords: Remaining useful life; Prognostic; Health indicator; Long short-term memory; Time delay neural network (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002466

DOI: 10.1016/j.ress.2023.109332

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