A Bayesian Approach to Diagnose Multiple Intermittent Faults

Logic reasoning approaches to fault diagnosis account for the fact that a component cj may fail intermittently by introducing a parameter gj that expresses the probability the component exhibits correct behavior. This component parameter gj , in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj is not known a priori. While proper estimation of gj can have a great impact on the diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, BARINEL, that computes exact estimations of gj as integral part of the posterior candidate probability computation. BARINEL’s diagnostic performance is evaluated for both synthetic systems and the Siemens software benchmark. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.