NDLI: Degradation Modeling and Maintenance Decisions Based on Bayesian Belief Networks

Content Provider	IEEE Xplore Digital Library
Author	Xinghui Zhang Jianshe Kang Tongdan Jin
Copyright Year	1963
Abstract	A variety of data-driven models focused on remaining lifetime prediction have been developed under condition-based monitoring framework. These models either assume an analytical formula for the underlying degradation path is known, or the number of degradation states could be determined subjectively. This paper proposes an adaptive discrete-state model to estimate system remaining lifetime based on Bayesian Belief Network (BBN) theory. The model consists of three steps: degradation state identification, degradation state characterization, and remaining life prediction. Our approach does not require an explicit distribution function to characterize the fault evolutionary process. Because the BBN model leverages the validity measures to determine the optimum state number, it avoids the state identification errors under limited feature data. The performance of the BBN model is validated and verified by actual and simulated bearing life data. Numerical examples show that the Bayesian degradation model outperforms a time-based maintenance policy both in cost and reliability.
Sponsorship	IEEE Reliability Society
Starting Page	620
Ending Page	633
Page Count	14
File Size	2493368
File Format	PDF
ISSN	00189529
Volume Number	63
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Degradation Hidden Markov models Predictive models Bayes methods Indexes Data models Maintenance engineering wavelet decomposition Bayesian network condition-based monitoring feature extraction remaining useful lifetime
Content Type	Text
Resource Type	Article
Subject	Safety, Risk, Reliability and Quality Electrical and Electronic Engineering

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