A Bayesian Model for Integrating Multiple Sources of Lifetime Information in System-Reliability Assessments

We present a Bayesian model for assessing the reliability of multicomponent systems. Novel features of this model are the natural manner in which lifetime data collected at either the component, subsystem, or system level are integrated with prior information at any level. The model allows pooling o...

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Bibliographic Details
Published in:Journal of quality technology 2011-04, Vol.43 (2), p.127-141
Main Authors: Reese, C. Shane, Wilson, Alyson G., Guo, Jiqiang, Hamada, Michael S., Johnson, Valen E.
Format: Article
Language:English
Subjects:
Applied sciences
Bayesian analysis
Censored Data
Complex systems
Exact sciences and technology
Expert Opinion
Lifetime Data
Markov Chain Monte Carlo
Multicomponent System
Multilevel Data
Operational research and scientific management
Operational research. Management science
Prior Information
Reliability
Reliability theory. Replacement problems
Studies
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Description
Summary:We present a Bayesian model for assessing the reliability of multicomponent systems. Novel features of this model are the natural manner in which lifetime data collected at either the component, subsystem, or system level are integrated with prior information at any level. The model allows pooling of information between similar components, the incorporation of expert opinion, and straightforward handling of censored data. The methodology is illustrated with two examples.
ISSN:0022-4065
2575-6230
DOI:10.1080/00224065.2011.11917851