%0 Conference Paper
%B Proceedings of the 21st international jont conference on Artifical intelligence
%D 2009
%T A new bayesian approach to multiple intermittent fault diagnosis
%A Rui Abreu
%A Zoeteweij, Peter
%A Van Gemund, Arjan JC
%C Pasadena, California, USA
%I Morgan Kaufmann Publishers Inc.
%P 653–658
%X <p>Logic reasoning approaches to fault diagnosis account for the fact that a component cjmay fail intermittently by introducing a parameter gj that ex presses 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 ongjis not known a priori. While proper estimation of gjcan 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 gjas 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.</p>
%8 July
%> https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/ijcai09-114.pdf