Reliability analysis for multi-state system based on triangular fuzzy variety subset bayesian networks

Abstract

Reliability is significant in describing the performance of a system. System reliability indicates the capability of a system to perform specified functions under definite conditions and within limited time period. System reliability analysis methods can help calculate the value of system reliability and determine the weakness of a system. Therefore, a system reliability analysis model, which is close to the actual system, should be established. This model will provide theoretical support for research, improvement, and maintenance. In recent years, system reliability theory is rapidly developed. Numerous system reliability analysis methods, such as fault tree analysis methods [5], binary decision diagram (BDD) analysis methods [6, 9], T-S fuzzy fault tree analysis methods [16] and Bayesian networks (BNs), are also emerging. Among the above methods, BN analysis methods [10, 2] are the most widely applied in system reliability analysis [17] and fault diagnosis [2] because of their advantages in both modeling capability and analysis process. The traditional BN reliability analysis methods are based on the hypothesis of two-state simple series–parallel systems [1,3]; these systems ignore the fuzziness of information and polymorphism of fault states in actual systems and consequently limit their application. In the past few years, further research used BN analysis methods to overcome the aforementioned problems. In [12], BNs were introduced to dynamic system reliability evaluation; moreover, traditional BN reliability analysis methods were further developed to improve the efficiency and accuracy of calculation and extend the application scope. BNs were combined with fuzzy set theory to predict the safety risk of subway operation and analyze Qin He Yabing ZHA Ruijun ZHAng Quan Sun Tianyu Liu