Bayes estimation of component-reliability from masked system-life data

Dennis K.J. Lin, John S. Usher, Frank M. Guess

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Summary & Conclusions - This paper estimates component reliability from masked series-system life data, viz, data where the exact component causing system failure might be unknown. It focuses on a Bayes approach which considers prior information on the component reliabilities. In most practical settings, prior engineering knowledge on component reliabilities is extensive. Engineers routinely use prior knowledge and judgment in a variety of ways. The Bayes methodology proposed here provides a formal, realistic means of incorporating such subjective knowledge into the estimation process. In the event that little prior knowledge is available, conservative or even non-informative priors, can be selected. The model is illustrated for a 2-component series system of exponential components. In particular it uses discrete-step priors because of their ease of development & interpretation. By taking advantage of the prior information, the Bayes point-estimates consistently perform well, ie, are close to the MLE. While the approach is computationally intensive, the calculations can be easily computerized.

Original languageEnglish (US)
Pages (from-to)233-237
Number of pages5
JournalIEEE Transactions on Reliability
Issue number2
StatePublished - 1996

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering


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