In fault diagnosis intermittent failure models are an important tool to adequately deal with realistic failure behavior. Current model-based diagnosis approaches 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 be critical to diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, coined BARINEL, that computes estimations of the g(j) as integral part of the posterior candidate probability computation using a maximum likelihood estimation approach. BARINEL'S diagnostic performance is evaluated for both synthetic systems, the Siemens software diagnosis benchmark, as well as for real-world programs. Our results show that our approach is superior to reasoning approaches based on classical persistent failure models, as well as previously proposed intermittent failure models. (C) 2010 Elsevier B.V. All rights reserved.
