Paper Info
Title
Degradation analysis with nonlinear exponential-dispersion process: Bayesian offline and online perspectives
Abstract
Exponential-dispersion (ED) process has been recently introduced and demonstrated as a promising degradation model, which can include classical Wiener, gamma, and inverse Gaussian (IG) processes as special cases. However, most related studies are based on offline and point estimation methods, which limit their capability for uncertainty quantification and online update inference. In this paper, nonlinear ED process models equipped with Bayesian offline and online inference methods are presented. Tweedie ED process models are studied for degradation analysis with accelerated factors and unit-to-unit variability. Bayesian offline method based on NO-U-Turn sampler (NUTS) algorithm and Bayesian online method based on particle filter are developed, respectively. The offline method is presented to enhance the ED process based degradation analysis with uncertainty quantification. The online method is developed for the applications with limited storage and computing resources, for which degradation observations are analyzed on-the-fly with improved efficiency. Effectiveness and characteristics of the proposed methods are demonstrated through a simulation study and two case studies.
Year
DOI
Venue
2022
10.1002/qre.3179
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
Keywords
DocType
Volume
Bayesian framework, covariates, degradation analysis, random effects, tweedie exponential-dispersion process
Journal
38
Issue
ISSN
Citations 
7
0748-8017
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yi Ding110037.68
Rong Zhu200.34
Weiwen Peng300.34
Min Xie4126396.98