Diagnosing multiple intermittent failures using maximum likelihood estimation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2010

Autores

Rui Abreu (Autor) FEUP; Arjan J C van Gemund (Autor) Outra

Revista

Título: Artificial Intelligence

Vol. 174 Nº 18

Páginas: 1481-1497

ISSN: 0004-3702

Editora: Elsevier

Indexação

ISI Web of Knowledge - 15 Citações

ISI Web of Science

Scopus - 25 Citações

COMPENDEX

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17