<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rui Abreu</style></author><author><style face="normal" font="default" size="100%">Zoeteweij, Peter</style></author><author><style face="normal" font="default" size="100%">Van Gemund, Arjan JC</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian Approach to Diagnose Multiple Intermittent Faults</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Twentieth International Workshop on Principles of Diagnosis (DX'09)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/dx09-1.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Stockholm, Swden</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Logic reasoning approaches to fault diagnosis account for the fact that a component cj may&lt;br /&gt;
fail intermittently by introducing a parameter gj that expresses the probability the component exhibits&lt;br /&gt;
correct behavior. This component parameter gj , in conjunction with a priori fault probability, is used in&lt;br /&gt;
a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj&lt;br /&gt;
is not known a priori. While proper estimation of gj can have a great impact on the diagnostic accuracy,&lt;br /&gt;
at present, only approximations have been proposed. We present a novel framework, BARINEL, that&lt;br /&gt;
computes exact estimations of gj as integral part of the posterior candidate probability computation.&lt;br /&gt;
BARINEL’s diagnostic performance is evaluated for both synthetic systems and the Siemens software&lt;br /&gt;
benchmark. Our results show that our approach is superior to approaches based on classical persistent&lt;br /&gt;
fault models as well as previously proposed intermittent fault models.&lt;/p&gt;
</style></abstract></record></records></xml>