  In this study, a Bayesian Network (BN) is considered to represent a nuclear
plant mechanical system degradation. It describes a causal representation of
the phenomena involved in the degradation process. Inference from such a BN
needs to specify a great number of marginal and conditional probabilities. As,
in the present context, information is based essentially on expert knowledge,
this task becomes very complex and rapidly impossible. We present a solution
which consists of considering the BN as a log-linear model on which
simplification constraints are assumed. This approach results in a considerable
decrease in the number of probabilities to be given by experts. In addition, we
give some simple rules to choose the most reliable probabilities. We show that
making use of those rules allows to check the consistency of the derived
probabilities. Moreover, we propose a feedback procedure to eliminate
inconsistent probabilities. Finally, the derived probabilities that we propose
to solve the equations involved in a realistic Bayesian network are expected to
be reliable. The resulting methodology to design a significant and powerful BN
is applied to a reactor coolant sub-component in EDF Nuclear plants in an
illustrative purpose.
