A graphical approach to identification of dependent failures

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Field data provide a rich source of information about the dependent failures whose omission from existing models can result in underestimation of the reliability of repairable systems, A graphical technique has been developed to highlight these events. This involves comparing the observed number of failures with the expected pattern under a null model of no common cause dependence, a non-homogeneous Poisson process with Weibull rate function. The derivation of the graph is outlined and its use as a screening tool is illustrated by applications to field data. The dependent failures identified are described and their engineering implications are discussed. The statistical power of the technique is evaluated for a range of alternative models of dependence, including simple shock models.
LanguageEnglish
Pages185-196
Number of pages11
JournalJournal of the Royal Statistical Society. Series D, The Statistician
Volume45
Issue number2
Publication statusPublished - 1996

Fingerprint

Dependent
Shock Model
Non-homogeneous Poisson Process
Repairable System
Statistical Power
Rate Function
Weibull
Screening
Null
Model
Engineering
Alternatives
Graph in graph theory
Range of data
Graphics

Keywords

  • common cause failures
  • dependent failures
  • exploratory data analysis
  • non-homogeneous Poisson process
  • reliability
  • repairable systems

Cite this

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A graphical approach to identification of dependent failures. / Walls, L.A.

In: Journal of the Royal Statistical Society. Series D, The Statistician, Vol. 45, No. 2, 1996, p. 185-196.

Research output: Contribution to journalArticle

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T1 - A graphical approach to identification of dependent failures

AU - Walls, L.A.

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AB - Field data provide a rich source of information about the dependent failures whose omission from existing models can result in underestimation of the reliability of repairable systems, A graphical technique has been developed to highlight these events. This involves comparing the observed number of failures with the expected pattern under a null model of no common cause dependence, a non-homogeneous Poisson process with Weibull rate function. The derivation of the graph is outlined and its use as a screening tool is illustrated by applications to field data. The dependent failures identified are described and their engineering implications are discussed. The statistical power of the technique is evaluated for a range of alternative models of dependence, including simple shock models.

KW - common cause failures

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SP - 185

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JO - Journal of the Royal Statistical Society. Series D, The Statistician

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