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.
Original language | English |
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Pages (from-to) | 185-196 |
Number of pages | 11 |
Journal | Journal of the Royal Statistical Society. Series D, The Statistician |
Volume | 45 |
Issue number | 2 |
Publication status | Published - 1996 |
Keywords
- common cause failures
- dependent failures
- exploratory data analysis
- non-homogeneous Poisson process
- reliability
- repairable systems