Monte Carlo simulation to solve fuzzy dynamic fault tree

摘要

Probabilistic risk analysis (PRA) is the most widely used method in risk analysis studies to look at the frequency and consequences of the occurrence of an undesirable events. It aims to analyze and identify the initiating events and accident sequences after their occurrence. FTA (Fault tree) is a common modeling technique in performing PRA for large and complex systems, which is based on static AND/OR gates. However, FTA presents limited modeling capabilities in the case of dynamic systems where they may cache a sequence and functional dependency. Dynamic fault tree (DFT) represents an alternative to model the dynamic failure mechanism. This paper provides a simulation technique based on MCS (Monte Carlo Simulations) to solve dynamic fault tree taking into account epistemic uncertainty in the determination of the failure rate of basic events. Based on the proposed method, the fuzzy distribution of the dynamic gates are estimated and finally an example is given to illustrate the method.