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

  • Carlos E. BuddeEmail author
  • Mariëlle Stoelinga
Conference paper
  • 75 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12040)

Abstract

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 (Open image in new window) reduces the number of samples when analysing rare events. Importance splitting is a Open image in new window 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 Open image in new window 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 Open image in new window.

Keywords

Minimal cut sets Rare event simulation Dynamic fault trees Importance splitting Fault tree analysis 

Supplementary material

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Formal Methods and ToolsUniversity of TwenteEnschedeThe Netherlands
  2. 2.Department of Software ScienceRadboud UniversityNijmegenThe Netherlands

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