Diagnosing multiple intermittent failures using maximum likelihood estimation

Abstract

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.