A Distributed Probabilistic Model for Fault Diagnosis

Fault diagnosis in complex systems is important due to the impact it may have for reducing breakage costs or for avoiding production losses in industrial systems. Several approaches have been proposed for fault diagnosis, some of which are based on Bayesian Networks. Bayesian Networks are an adequate formalism for representing and reasoning under uncertainty conditions, however, they do not scale well for complex systems. For overcoming this limitation, researchers have proposed Multiply Sectioned Bayesian Networks. These are an extension of the Bayesian Networks for representing large domains, while ensuring the network inference in an efficient way. In this work we propose a distributed method for fault diagnosis in complex systems using Multiply Sectioned Bayesian Networks. The method was tested in the detection of multiple faults in combinational logic circuits showing comparable results with the literature in terms of accuracy, but with a significant reduction in the runtime.