Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity. (August 2018)

• Record Type: Journal Article
• Authors: Wen, Yuxin; Wu, Jianguo; Das, Devashish; Tseng, Tzu-Liang(Bill)
• Is Part Of: Reliability engineering & system safety. Volume 176(2018)
• Page Start: 113
• Page End: 124
• Publication Date: 2018-08
• Subjects: Wiener process -- Multiple change-point model -- Degradation modeling -- Remaining useful life prediction; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.04.005
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A multiple change-point Wiener process based prognostic framework is proposed. To consider unit-to-unit heterogeneity, a fully Bayesian approach is developed. An empirical two-stage process is proposed for model estimation. Online individual model updating is achieved through an exact recursive algorithm. The effectiveness is demonstrated through simulation and real case studies. Abstract: Degradation modeling is critical for health condition monitoring and remaining useful life prediction (RUL). The prognostic accuracy highly depends on the capability of modeling the evolution of degradation signals. In many practical applications, however, the degradation signals show multiple phases, where the conventional degradation models are often inadequate. To better characterize the degradation signals of multiple-phase characteristics, we propose a multiple change-point Wiener process as a degradation model. To take into account the between-unit heterogeneity, a fully Bayesian approach is developed where all model parameters are assumed random. At the offline stage, an empirical two-stage process is proposed for model estimation, and a cross-validation approach is adopted for model selection. At the online stage, an exact recursive model updating algorithm is developed for online individual model estimation, and an effective Monte Carlo simulation approach is proposed for RUL prediction. The effectiveness of the proposed method is demonstrated through thorough simulation studies and real case study.