Quantitative risk analysis model of integrating fuzzy fault tree with Bayesian Network

Abstract

In this paper, a new quantitative risk analysis model of integrating fuzzy fault tree (FFT) with Bayesian Network (BN) is proposed. The first step involves describing a fuzzy fault tree analysis technique based on the Takagi and Sugeno model. The second step proposes the translation rules for converting FFT into BN. Based on this, the integration algorithm is demonstrated by an offshore fire case study. The example clearly shows that FFT can be directly converted into BN and the classical parameters of FFT can be obtained by the basic inference techniques of BN. By using the advantages of both techniques, the model of integrating FFT with BN is more flexible and useful than traditional fault tree model. This new model not only can be used for describing the causal effect of accident escalation but also for computing the occurrence probability of accident based on historical data and fuzzy logic.