Dependent masking and system life data analysis: Bayesian inference for two-component systems

Irwin Guttman, Dennis K.J. Lin, B. Reiser, John S. Usher

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

Data from field operations of a system is often used to estimate the reliability of components. Under ideal circumstances, this system field data contains the time to failure along with information on the exact component responsible for the system failure. However, in many cases, the exact component causing the failure of the system cannot be identified, and is considered to be masked. Previously developed models for estimation of component reliability from masked system life data have been based upon the assumption that masking occurs independently of the true cause of system failure. In this paper we develop a Bayesian methodology for estimating component reliabilities from masked system life data when the probability of masking is dependent upon the true cause of system failure. The Bayesian approach is illustrated for the case of a two-component system of exponentially distributed components.

Original languageEnglish (US)
Pages (from-to)87-100
Number of pages14
JournalLifetime Data Analysis
Volume1
Issue number1
DOIs
StatePublished - Mar 1 1995

Fingerprint

Masking
Bayesian inference
Information Systems
Data analysis
Dependent
Computer Communication Networks
Bayes Theorem
Life
Bayesian Approach
Methodology

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

Cite this

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Dependent masking and system life data analysis : Bayesian inference for two-component systems. / Guttman, Irwin; Lin, Dennis K.J.; Reiser, B.; Usher, John S.

In: Lifetime Data Analysis, Vol. 1, No. 1, 01.03.1995, p. 87-100.

Research output: Contribution to journalArticle

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