Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process

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.