1887

n South African Journal of Industrial Engineering - Stochastic model for common cause failures and human error

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Abstract

A consistent asymptotic normal (CAN) estimator and confidence limits for the steady-state availability of series and parallel systems subject to unit failures, common-cause shock (CCS) failures and human error are studied. This paper also deals with the estimation from a Bayesian viewpoint with a number of prior distributions assumed for the unknown parameters in the system, which reflect different degrees of belief on the failure mechanisms. A Monte Carlo simulation is used to derive the posterior distribution for the steady-state availability and subsequently the highest posterior density (HPD) intervals. A numerical example illustrates the results.

A consistent asymptotic normal (CAN) estimator and confidence limits for the steady-state availability of series and parallel systems subject to unit failures, common-cause shock (CCS) failures and human error are studied. This paper also deals with the estimation from a Bayesian viewpoint with a number of prior distributions assumed for the unknown parameters in the system, which reflect different degrees of belief on the failure mechanisms. A Monte Carlo simulation is used to derive the posterior distribution for the steady-state availability and subsequently the highest posterior density (HPD) intervals. A numerical example illustrates the results.

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/content/indeng/16/1/EJC46093
2005-05-01
2016-12-03
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