Mapping Fault Tree into Bayesian Network in safety analysis of process system

Abstract

Fault tree method have become a popular technique that used widely in the safety analysis of process systems, this method aimed to identify and assessing hazards of complex systems. However, Fault Tree analysis is not flexible enough to incorporate new knowledge or evidence also Fault Tree have difficult to handle with updating probability of basic events and the dependent between primary events and multi-states variables. A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies. This paper focused on using Bayesian networks to handle with the limitations of Fault Tree and the uncertainty of the accident and used the ability of Bayesian Networks to represent the dependence failure and multi-states variables. An examples is used to illustrate the application and compare the results of both Fault Tree and Bayesian Network techniques.

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