Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic boundary1. Issue 21 (2015) - Atoui, Mohamed Amine
Abstract: The purpose of this article is to present a method for Fault Detection and Diagnosis (FDD) with Bayesian network, and more particularly with Conditional Gaussian Network (CGN). A classical problem of FDD with supervised classification is when Normal Operating Conditions class is integrated, the false alarm rate is not guarantee by the classifier. The interest of the proposed method is to introduce a probabilistic limit on the Normal Operating Conditions class, allowing to respect a given false alarm rate. Performances of this method are evaluated on data of a benchmark example: the Tennessee Eastman Process. Three kinds of faults are taken into account on this complex process.