A New Bayesian Approach to Multiple Intermittent Fault Diagnosis

Logic reasoning approaches to fault diagnosis account for the fact that a component c(j) may fail intermittently by introducing a parameter g(j) that expresses the probability the component exhibits correct behavior. This component parameter g(j), in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on g(j) is not known a priori. While proper estimation of g(j) 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 g(j) as integral part of the posterior candidate probability computation. BARINEL's diagnostic performance is evaluated for both synthetic and real software systems. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.