AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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A New Bayesian Approach to Multiple Intermittent Fault Diagnosis
Rui Abreu, Peter Zoeteweij, Arjan J.C. van Gemund

Last modified: 2009-06-25


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 usedin 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 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.


Diagnosis and Abductive Reasoning; Model-Based Reasoning; Reasoning with Beliefs; Uncertainty in AI

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