Bayes inference for general repairable systems

Rong Pan, Steven E. Rigdon

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Models for repairable systems are often characterized by the assumed effect of a failure and the subsequent repair. As-bad-as-old models lead to the nonhomogeneous Poisson process and as-good-as-new models lead to the renewal process. We study Bayesian methods for some models that are a compromise between the bad-as-old and the good-as-new models. For the case of multiple systems, we consider a hierarchical Bayes model. We use Markov chain Monto Carlo methods to approximate properties of the posterior distributions.

Original languageEnglish (US)
Pages (from-to)82-94
Number of pages13
JournalJournal of Quality Technology
Volume41
Issue number1
StatePublished - Jan 2009

Fingerprint

Markov processes
Repairable system
Inference
Repair
Markov chain
Posterior distribution
Compromise
Hierarchical Bayes model
Renewal process
Poisson process
Bayesian methods

Keywords

  • Hierarchical bayes model
  • Imperfect repair
  • Markov chain Monte Carlo
  • Power law process

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research

Cite this

Bayes inference for general repairable systems. / Pan, Rong; Rigdon, Steven E.

In: Journal of Quality Technology, Vol. 41, No. 1, 01.2009, p. 82-94.

Research output: Contribution to journalArticle

Pan, Rong ; Rigdon, Steven E. / Bayes inference for general repairable systems. In: Journal of Quality Technology. 2009 ; Vol. 41, No. 1. pp. 82-94.
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