A graphical approach to identification of dependent failures

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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.
Original languageEnglish
Pages (from-to)185-196
Number of pages11
JournalJournal of the Royal Statistical Society. Series D, The Statistician
Issue number2
Publication statusPublished - 1996


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


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