Document Type
Article
Publication Date
6-15-2017
Abstract
Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies.
Recommended Citation
Y. Wen, J. Wu and Y. Yuan, “Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction,” IEEE Transactions on Reliability, vol. 66, no. 3, pp. 924-938, Sept. 2017. https://doi.org/10.1109/TR.2017.2710319
Copyright
© 2017 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 Reliability, volume 66, issue 3, in 2017 following peer review. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/TR.2017.2710319.