Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 185, Issue -, Pages 372-382Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2019.01.006
Keywords
RUL prediction; PHM; Recurrent neural network; Nonlinear deterioration
Funding
- National Natural Science Foundation of China [51875436, 61633001]
- China Postdoctoral Science Foundation [2018M631145]
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Remaining useful life (RUL) prediction is a key process for prognostics and health management (PHM). However, conventional model-based methods and data-driven methods for RUL prediction are bad at a very complex system with multiple components, multiple states and therefore extremely large amount of parameters. In order to solve the problem, a general two-step solution is proposed in this paper. In the first step, kernel principle component analysis (KPCA) is applied for nonlinear feature extraction. Then, a novel recurrent neural network called gated recurrent unit (GRU) is presented as the second step to predict RUL. GRU network is capable of describing a very complex system because of its specially designed structure. The effectiveness of the proposed solution for RUL prediction of a nonlinear degradation process is proved by a case study of commercial modular aero-propulsion system simulation data (C-MAPSS-Data) from NASA. Results also show that the proposed method requires less training time and has better prediction accuracy than other data-driven methods.
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