Graphical models for diagnosis knowledge representation and inference

One popular approach for fault diagnosis is based on reasoning about the behavior of a system in failure space. Diagnosis is performed by considering a set of observations (or symptoms) and by explaining it in terms of a set of root causes. There are many modeling methods to capture the system´s faulty behavior, such as behavioral Petri nets, multi-signal flow graphs, and Bayesian networks. In this paper, we will investigate the equivalence of these three modeling formalism by way of application to a car engine diagnosis problem, and discuss the advantages and disadvantages of each method