Diagnosing multiple intermittent failures using maximum likelihood estimation

• Type: Article in International Scientific Journal
• Date: 2010

Authors

Rui Abreu (Author) FEUP; Arjan J C van Gemund (Author) Other

Journal

Title: Artificial Intelligence

Vol. 174 No. 18

Pages: 1481-1497

ISSN: 0004-3702

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 15 Citations

ISI Web of Science

Scopus - 25 Citations

COMPENDEX

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-003-0AY

DOI: 10.1016/j.artint.2010.09.003

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17