Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Abstract

Multivariable stochastic degradation system (MSDS) is quite common in indus-tries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.

Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

“…In the second level, the stacked‐LSTM model with the sliding window method is used to predict the final predicted values. Metaheuristics approaches such as genetic algorithm and particle swarm optimization have been widely used for parameter estimation 44 . In this study, the hyperparameters of each model are derived by using the DE algorithm.…”

“…Metaheuristics approaches such as genetic algorithm and particle swarm optimization have been widely used for parameter estimation. 44 In this study, the hyperparameters of each model are derived by using the DE algorithm. The detail of the DE algorithm can be found in the literature.…”