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Predictive inference for system reliability after common-cause component failures.

Coolen, F.P.A. and Coolen-Maturi, T. (2015) 'Predictive inference for system reliability after common-cause component failures.', Reliability engineering & system safety., 135 . pp. 27-33.


This paper presents nonparametric predictive inference for system reliability following common-cause failures of components. It is assumed that a single failure event may lead to simultaneous failure of multiple components. Data consist of frequencies of such events involving particular numbers of components. These data are used to predict the number of components that will fail at the next failure event. The effect of failure of one or more components on the system reliability is taken into account through the system׳s survival signature. The predictive performance of the approach, in which uncertainty is quantified using lower and upper probabilities, is analysed with the use of ROC curves. While this approach is presented for a basic scenario of a system consisting of only a single type of components and without consideration of failure behaviour over time, it provides many opportunities for more general modelling and inference, these are briefly discussed together with the related research challenges.

Item Type:Article
Keywords:Common-cause failures, Lower and upper probabilities, Nonparametric predictive inference, ROC curves, Survival signature, System reliability.
Full text:(AM) Accepted Manuscript
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Publisher statement:NOTICE: this is the author’s version of a work that was accepted for publication in Reliability Engineering & System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering & System Safety, 135, March 2015, 10.1016/j.ress.2014.11.005.
Date accepted:08 November 2014
Date deposited:28 November 2014
Date of first online publication:15 November 2014
Date first made open access:No date available

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