Reliability assessment of systems subject to interval-valued probabilistic common cause failure by evidential networks

• Published in: Journal of intelligent & fuzzy systems Vol. 36; no. 4; pp. 3711 - 3723
• Main Authors: Zuo, Lin, Xiahou, Tangfan, Liu, Yu
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2019; Sage Publications Ltd
• Subjects: Algorithms; Common cause failures; Component reliability; Epistemology; Failure; Mass distribution; Network reliability; Reliability analysis; Reliability engineering; System reliability; Uncertainty; evidential networks; epistemic uncertainty; Evidence theory; interval-valued probabilistic common cause failure
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-18290

 Reliability assessment of complex engineered systems is challenging as epistemic uncertainty and common cause failure (CCF) are inevitable. The probabilistic common cause failure (PCCF), which characterizes the simultaneous failures of multiple components with distinguished chances, is a generalized model of traditional CCF model. To accurately assess system reliability, it is of great significance to take both the effects of PCCF and the epistemic uncertainty of components’ state probabilities into account. In this paper, an evidential network model is proposed to assess system reliability with interval-valued PCCFs and epistemic uncertainty associated with components’ state probabilities. The procedures of computing the mass distribution of a component suffering from multiple PCCFs are detailed. The inference algorithm in the evidential network is, then, used to calculate the mass distribution of the entire system. The Birnbaum importance measure is also defined to identify the weak components under PCCFs and epistemic uncertainty. A safety instrumented system is exemplified to demonstrate the effectiveness of the proposed evidential network model in terms of coping with PCCFs and epistemic uncertainty. The importance results show that both the epistemic uncertainty associated with components’ state probabilities and PCCFs have impact on components’ importance.