A reliability analysis technique for complex systems with retaining fuzzy information

Abstract

This paper proposed a method for the reliability analysis of systems characterized by fuzzy failure probabilities and intricate failure behaviors, while retaining the fuzzy information throughout the analysis process. Specifically, we introduced a combinational modeling method that integrates generalized stochastic Petri nets (GSPN) and dynamic fault trees (DFT) to capture the dynamic failure behaviors and address the limitations of DFT modeling. This combinational approach is capable of capturing more sophisticated failure behaviors, such as competing failures and global failure propagation patterns. Furthermore, we propose an Monte Carlo technique based on endpoint sampling to enable quantitative analysis of GSPN‐based composite models, which preserves the fuzzy fault information of the system. Finally, we demonstrate the effectiveness of the proposed method through an example of a flight control system.