<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rui Abreu</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%">Diagnosing multiple intermittent failures using maximum likelihood estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/aij10_0.pdf</style></url><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/aij10_1.pdf</style></url></related-urls></urls><number><style face="normal" font="default" size="100%">18</style></number><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">174</style></volume><pages><style face="normal" font="default" size="100%">1481–1497</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&quot;j may fail intermittently by introducing a parameter g&quot;j that expresses the probability the component exhibits correct behavior. This component parameter g&quot;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&quot;j is not known a priori. While proper estimation of g&quot;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&quot;j as integral part of the posterior candidate probability computation using a maximum likelihood estimation approach. Barinel'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.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue></record></records></xml>