Automated Rare Event Simulation for Fault Tree Analysis via Minimal Cut Sets

Monte Carlo simulation is a common technique to estimate dependability metrics for fault trees. A bottleneck in this technique is the number of samples needed, especially when the interesting events are rare and occur with low probability. Rare Event Simulation () reduces the number of samples when analysing rare events. Importance splitting is a method that spawns more simulation runs from promising system states. How promising a state is, is indicated by an importance function, which concentrates the information that makes this method efficient. Importance functions are given by domain and experts. This hinders re-utilisation and involves decisions entailing potential human error. Focusing in (general) fault trees, in this paper we automatically derive importance functions based on the tree structure. For this we exploit a common fault tree concept, namely cut sets: the more elements from a cut set have failed, the higher the importance. We show that the cut-set-derived importance function is an easy-to-implement and simple concept, that can nonetheless compete against another (more involved) automatic importance function for.