Probabilistic Safety Assessment (PSA) is a technique to quantify the risk
associated with complex systems like Nuclear Power Plants (NPPs), chemical
industries, aerospace industry, etc. PSA aims at identifying the possible undesirable
scenarios that could occur in a plant, along with the likelihood of their occurrence
and the consequences associated with them. PSA of NPPs is generally performed
through Fault Tree (FT) and Event Tree (ET) approach. FTs are used to evaluate the
unavailability or frequency of failure of various systems in the plant, especially
those that are safety critical. Some of the limitations of FTs and ETs are consideration of constant failure/repair data for components. Also, the dependency
between the component failures is handled in a very conservative manner using beta
factor, alpha factors, etc. Recently, the trend is shifting toward the development of
Bayesian Network (BN) model of FTs. BNs are directed acyclic graphs and work
on the principles of probability theory. The paper highlights how to develop BN
from FT and how it can be used to develop a BN model of the FT of Isolation
Condenser (IC) of the advanced reactor and incorporate the system component
indicator status into the BN. The indicator status would act like evidence to the
basic events, thus updating their probabilities