Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement

• Published in: IEEE transactions on automation science and engineering Vol. 16; no. 2; pp. 613 - 628
• Main Authors: Wen, Yuxin, Wu, Jianguo, Zhou, Qiang, Tseng, Tzu-Liang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Bayesian analysis; Computational modeling; Computer simulation; Data models; Decision making; Degradation; Degradation modeling; exact Bayesian inference; Exponential functions; Functionals; Model updating; Monitoring; multiple-change-point (CP) model; Numerical models; Parameters; Predictive models; prognostics; Random variables; Regression models; remaining useful life (RUL) prediction; Statistical analysis; Statistical inference; Stochastic processes; Useful life
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2018.2844204

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 exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies.