Common-Cause Failure Treatment in Event Assessment: Basis for a Proposed New Model

Abstract

Event assessment is an application of probabilistic risk assessment in which observed equipment failures and outages are mapped into the risk model to obtain a numerical estimate of the event’s risk significance. In this paper, we focus on retrospective assessments to estimate the risk significance of degraded conditions such as equipment failure accompanied by a deficiency in a process such as maintenance practices. In modeling such events, the basic events in the risk model that are associated with observed failures and other off-normal situations are typically configured to be failed, while those associated with observed successes and unchallenged components are assumed capable of failing, typically with their baseline probabilities. This is referred to as the failure memory approach to event assessment. The conditioning of common-cause failure probabilities for the common cause component group associated with the observed component failure is particularly important, as it is insufficient to simply leave these probabilities at their baseline values, and doing so may result in a significant underestimate of risk significance for the event. Past work in this area has focused on the mathematics of the adjustment. In this paper, we review the Basic Parameter Model for common-cause failure, which underlies most current risk modelling, discuss the limitations of this model with respect to event assessment, and introduce a proposed new framework for common-cause failure, which uses a Bayesian network to model underlying causes of failure, and which has the potential to overcome the limitations of the Basic Parameter Model with respect to event assessment.

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