Diagnosing multiple intermittent failures using maximum likelihood estimation

  • Authors:

  • Rui Abreu;Arjan J. C. van Gemund

  • Affiliations:

  • Department of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal;Embedded Software Group, Delft University of Technology, Faculty of Electrical Eng., Math., and CS, The Netherlands

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2010

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Abstract

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'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.