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.
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
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