Rare-Event Estimation for Dynamic Fault Trees

Abstract

Article describes the results of the development and using of Rare-Event Monte-Carlo Simulation Algorithms for Dynamic Fault Trees Estimation. For Fault Trees estimation usually analytical methods are used (Minimal Cut sets, Markov Chains, etc.), but for complex models with Dynamic Gates it is necessary to use Monte-Carlo simulation with combination of Importance Sampling method. Proposed article describes approach for this problem solution according for specific features of Dynamic Fault Trees. There are assumed, that failures are non-repairable with general distribution functions of times to failures (there may be Exponential distribution, Weibull, Normal and Log-Normal, etc.). Expessions for Importance Sampling Re-Calculations are proposed and some numerical results are considered