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.

In this paper, a quantitative risk analysis (QRA) model incorporating human and organizational factor is presented by integrating Fault Tree (FT) with Bayesian Network (BN). FT is used to model the factors how to contribute to the final failures. BN extends the causal chain of basic events to potential human and organizational roots and provides a more precise quantitative links between the event nodes. In order to define the conditional probability table of BN, fuzzy Analytical Hierarchy Process (AHP) is integrated with a decomposition method. The fuzzy AHP helps to reduce the subjective biases by avoiding the need to spell out explicit probability values for the variables' states. The decomposition method breaks the complexity by allowing conditioning on each of the parent nodes separately. The new QRA model is demonstrated on an offshore fire case study. By exploiting the advantages of both models, the method of combining FT and BN is normally a more detailed risk model with higher resolution, comparing with traditional QRA.