A systematic approach to estimate an inter-unit common-cause failure probability. (November 2021)

• Record Type: Journal Article
• Authors: Soga, Shota; Higo, Eishiro; Miura, Hiromichi
• Is Part Of: Reliability engineering & system safety. Volume 215(2021)
• Publication Date: 2021-11
• Subjects: Inter-unit Common Cause Failure -- Asymmetric Common Cause Failure Analysis -- Staggered Test Scheme -- Common Cause Coupling Factor Commonality -- CCF Basic Event Aggregation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107802
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Propose a systematic approach to model inter-unit common-cause failures Applicable to components across units under non-identical conditions Explicitly consider two sources of asymmetry in common-cause failures Successfully reduce the conservativeness in existing modeling approaches Provide rigorous mathematical justification for the proposed approach Abstract: Inter-unit common-cause failure (CCF) analysis is one of the technical issues for a probabilistic risk assessment for multiple nuclear reactors in a site. It is because inter-unit CCF events are rare and the symmetric assumption in intraunit CCF analysis is no longer valid. To overcome these difficulties, we propose a systematic approach to estimate the inter-unit CCF probability using conventional CCF databases and engineering judgments. The proposed approach explicitly considers two sources of asymmetry in inter-unit CCF: the commonalities of common-cause coupling factors and surveillance tests. The proposed approach starts from a symmetric CCF probability of components under a nonstaggered test scheme and extends it to take the asymmetry into account. The proposed approach is applied to the CCF analysis of the hypothetical case of three nonidentical emergency diesel generators and is compared with the conventional CCF analysis. The proposed approach can reduce the conservativeness in the inter-unit CCF probabilities estimated by the conventional CCF analysis and improve the quality of multiunit probabilistic risk assessment (PRA), where inter-unit CCF events significantly contribute to the site risk. The proposed approach demonstrates its applicability to an inter-unit CCF analysis where the asymmetries play an important role in inter-unit CCF probability.