Fault Diagnosis in an Industrial Process Using Bayesian Networks: Application of the Junction Tree Algorithm

In this paper we present a Bayesian Network for fault diagnosis used in an industrial tanks system. We obtain the Bayesian Network first and later based on this, we build a defined structure as Junction Tree. This tree is where we spread the probabilities with the algorithm known as LAZYAR (also Junction Tree). Nowadays the state of the art in inference algorithms in Bayesian Networks is the Junction Tree algorithm. We prove empirically through a case study as the Junction Tree algorithm has better performance with regard to the traditional algorithms as the Polytree.