@conference {abreu9bayesian,
	title = {A Bayesian Approach to Diagnose Multiple Intermittent Faults},
	booktitle = {Proceedings of the Twentieth International Workshop on Principles of Diagnosis (DX{\textquoteright}09)},
	year = {2009},
	month = {June},
	address = {Stockholm, Swden},
	abstract = {<p>Logic reasoning approaches to fault diagnosis account for the fact that a component cj may<br />
fail intermittently by introducing a parameter gj that expresses the probability the component exhibits<br />
correct behavior. This component parameter gj , in conjunction with a priori fault probability, is used in<br />
a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj<br />
is not known a priori. While proper estimation of gj can have a great impact on the diagnostic accuracy,<br />
at present, only approximations have been proposed. We present a novel framework, BARINEL, that<br />
computes exact estimations of gj as integral part of the posterior candidate probability computation.<br />
BARINEL{\textquoteright}s diagnostic performance is evaluated for both synthetic systems and the Siemens software<br />
benchmark. Our results show that our approach is superior to approaches based on classical persistent<br />
fault models as well as previously proposed intermittent fault models.</p>
},
	attachments = {https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/dx09-1.pdf},
	author = {Rui Abreu and Zoeteweij, Peter and Van Gemund, Arjan JC}
}