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.
Original language | English (US) |
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Article number | 109332 |
Journal | Reliability Engineering and System Safety |
Volume | 237 |
DOIs | |
State | Published - Sep 2023 |
Externally published | Yes |
Keywords
- Health indicator
- Long short-term memory
- Prognostic
- Remaining useful life
- Time delay neural network
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering