RESEARCH PAPER
Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network
1
School of Automation Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China
2
Center for System Reliability and Safety, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China
3
Institute for Risk and Reliability, Leibniz University Hannover, Callinstr. 34, Hannover, 30167, Germany
Publication date: 2020-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(1):111–120
KEYWORDS
ABSTRACT
When dealing with modern complex systems, the relationship existing between components can lead to the appearance of various
dependencies between component failures, where multiple items of the system fail simultaneously in unpredictable fashions. These
probabilistic common cause failures affect greatly the performance of these critical systems. In this paper a novel methodology
is developed to quantify the importance of common cause failures when hybrid uncertainties are presented in systems. First, the
probabilistic common cause failures are modeled with Bayesian networks and are incorporated into the system exploiting the α
factor model. Then, probability-boxes (bound analysis method) are introduced to model the hybrid uncertainties and quantify the
effect of uncertainties on system reliability. Furthermore, an extended Birnbaum importance measure is defined to identify the
critical common cause failure events and coupling impact factors when uncertainties are expressed by probability-boxes. Finally,
the effectiveness of the method is demonstrated through a numerical example
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