  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
