Quantitative risk assessment through hybrid causal logic approach

Abstract

In this paper, a hybrid causal logic (HCL) model is improved by mapping a fuzzy fault tree (FFT) into a Bayesian network (BN). The first step is to substitute an FFT for the traditional FT. The FFT is based on the Takagi–Sugeno model and the translation rules needed to convert the FFT into a BN are derived. The proposed model is demonstrated in a study of a fire hazard on an offshore oil production facility. It is clearly shown that the FFT can be directly converted into a BN and that the parameters of the FFT can be estimated more accurately using the basic inference techniques of a BN. The improved HCL approach is able to both accurately determine how failures cause an undesired problem using FFT and also model non-deterministic cause–effect relationships among system elements using the BN.