Reliability modeling and analysis of complex multi-state system based on interval fuzzy Bayesian network

With the increasing complexity and large size of modern advanced engineering systems, the traditional reliability analysis and evaluation technology which is based on large number of sample data cannot meet the demand of complex system. Aiming at the engineering application requirement, this paper focuses on the reliability modeling and analysis of complex system with uncertainties and failure dependencies. Due to the diversity of input information and the system failure factors, and system redundancies, the uncertainty and common cause failure (CCF) have become the most important factors for reliability analysis and evaluation of complex system. In consideration of the epistemic uncertainty caused by lack of probability statistical information, the fuzzy theory is employed to express the fuzzy information of system, and the basic events failure probabilities are described by interval-valued fuzzy numbers. Taking account of the influence of CCF to system reliability and the widespread presence of MSS in engineering practices, the CCF is quantified by the beta factor parameter model and integrated to Bayesian Network (BN) model through a new defined common cause node. Finally, a comprehensive method for reliability modeling and assessment of a multi-state system (MSS) with CCFs based on interval-valued fuzzy BN is proposed by taking the advantage of graphic representation and uncertainty reasoning of BN. The method has applied to the transmission system of two-axis positioning mechanism of a satellite antenna to demonstrate its effectiveness and capability for directly calculating the system reliability on the basis of multi-state probabilities of components. It has shown that the method proposed has done further improvement of the theory for reliability analysis of complex system and can realize its engineering application.