Computer Engineering Commons^™

Multiple-Change-Point Modeling And Exact Bayesian Inference Of Degradation Signal For Prognostic Improvement, Yuxin Wen, Jianguo Wu, Qiang Matthew Zhou, Tzu-Liang Bill Tseng

Engineering Faculty Articles and Research

Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to …