A new procedure based on mutual information for fault diagnosis of industrial systems

Abstract : The purpose of this article is to present a new procedure for industrial process diagnosis. This method is based on bayesian classifiers. A feature selection is done before the classification between the different faults of a process. The feature selection is based on a new result about mutual information that we demonstrate. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these 3 faults. Results are given and compared on the same data with those of other published methods
Type de document :
Communication dans un congrès
Workshop on Advanced Control and Diagnosis (ACD'06), 2006, Nancy, France. 2006
Liste complète des métadonnées

Littérature citée [17 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00517015
Contributeur : Sylvain Verron <>
Soumis le : lundi 13 septembre 2010 - 13:45:06
Dernière modification le : lundi 5 février 2018 - 15:00:08
Document(s) archivé(s) le : jeudi 30 juin 2011 - 13:27:27

Fichier

verron06c.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00517015, version 1

Collections

Citation

Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. A new procedure based on mutual information for fault diagnosis of industrial systems. Workshop on Advanced Control and Diagnosis (ACD'06), 2006, Nancy, France. 2006. 〈inria-00517015〉

Partager

Métriques

Consultations de la notice

116

Téléchargements de fichiers

63