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A pragmatic approach to estimate alpha factors for common cause failure analysis

机译:一种实用的方法来估算常见原因故障分析的alpha因子

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摘要

Most of the modern technological systems are deployed with high redundancy but still they fail mainly on account of common cause failures (CCF). Various models such as Beta Factor, Multiple Greek Letter, Binomial Failure Rate and Alpha Factor exists for estimation of risk from common cause failures. Amongst all, alpha factor model is considered most suitable for high redundant systems as it arrives at common cause failure probabilities from a set of ratios of failures and the total component failure probability Qr- In the present study, alpha factor model is applied for the assessment of CCF of safety systems deployed at two nuclear power plants. A method to overcome the difficulties in estimation of the coefficients viz., alpha factors in the model, importance of deriving plant specific alpha factors and sensitivity of common cause contribution to the total system failure probability with respect to hazard imposed by various CCF events is highlighted. An approach described in NUREG/CR-5500 is extended in this study to provide more explicit guidance for a statistical approach to derive plant specific coefficients for CCF analysis especially for high redundant systems. The procedure is expected to aid regulators for independent safety assessment.
机译:大多数现代技术系统都以高冗余性进行部署,但是它们仍然主要由于常见原因故障(CCF)而失败。存在各种模型,例如Beta因子,希腊文字母,二项式故障率和Alpha因子,可用于估计常见原因故障的风险。其中,α因子模型被认为最适合于高冗余系统,因为它可以从一组故障比率和总组件故障概率Qr得出共因故障概率。在本研究中,α因子模型用于评估部署在两个核电厂的安全系统的CCF评估。着重介绍了一种克服以下困难的方法:估算模型中的系数,α因子,推导特定于工厂的α因子的重要性以及因各种CCF事件造成的危害而导致的总原因对总系统故障概率的敏感性。 。这项研究扩展了NUREG / CR-5500中描述的方法,为统计方法提供更明确的指导,以导出工厂特定系数用于CCF分析,尤其是对于高冗余系统。该程序有望帮助监管机构进行独立的安全评估。

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