@article {abreu2010diagnosing,
	title = {Diagnosing multiple intermittent failures using maximum likelihood estimation},
	journal = {Artificial Intelligence},
	volume = {174},
	number = {18},
	year = {2010},
	pages = {1481{\textendash}1497},
	publisher = {Elsevier},
	abstract = {<p>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{\textquoteright}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.</p>
},
	attachments = {https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/aij10_0.pdf , https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/aij10_1.pdf},
	author = {Rui Abreu and Van Gemund, Arjan JC}
}