Document Type
Article
Publication Date
6-29-2018
Abstract
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.
Recommended Citation
Y. Wen, J. Wu, Q. Zhou and B. Tseng, “Multiple-Change-Point Modeling and Exact Bayesian Inference of Degradation Signal for Prognostic Improvement,” IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 613-628, April 2019. https://doi.org/10.1109/TASE.2018.2844204
Copyright
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in IEEE Transactions on Automation Science and Engineering, volume 16, issue 2, in 2018 following peer review. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/TASE.2018.2844204.