Uncertainty Propagation in Importance Analysis of Fault Trees  
Author Jiahao Zhang

 

Co-Author(s) Junjun Zheng; Hiroyuki Okamura; Tadashi Dohi

 

Abstract This paper presents an importance analysis method based on the continuous-time Markov chain (CTMC) when considering uncertainty propagation for component importance. The importance analysis is widely used for computing the importance of system components that contributes to the whole system. By judging the order of component importance, the most important component can be figured out. However, the statistic errors which are also called uncertainty in the estimation of model parameters may bring unexpected effects on the final output of system performance, which can also affect the order of component importance. In this paper, we focus on the Birnbaum importance measure with the Bayes method solving the effects of uncertainty propagation in fault tree. In particular, referring to the idea of the PageRank method, we present an importance analysis method based on stationary solution of CTMC for estimating the importance of system components when the model is given by fault tree.

 

Keywords Importance analysis, uncertainty propagation, fault tree, Birnbaum importance measure, Bayes theorem
   
    Article #:  RQD26-107
 

Proceedings of 26th ISSAT International Conference on Reliability & Quality in Design
Virtual Event

August 5-7, 2021