Contribution of fuzzy reasoning method to knowledge integration in a defect recognition system

Authors:
Vincent Bombardier;Cyril Mazaud;Pascal Lhoste;Raphaël Vogrig
Affiliations:
Centre de Recherche en Automatique de Nancy (CRAN), CNRS UMR no. 7039, Faculté des Sciences, Bd des Aiguillettes, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France;Centre de Recherche en Automatique de Nancy (CRAN), CNRS UMR no. 7039, Faculté des Sciences, Bd des Aiguillettes, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France and LuxScan Technologie ...;Ecole Nationale Supérieure en Génie des Systèmes Industriels (ENSGSI), Equipe de Recherche sur les Processus Innovatifs (ERPI), EA no. 3767, 8 rue Bastien Lepage, BP 90647, 54010 NA ...;LuxScan Technologies, ZARE Ouest, L 4384 Ehlerange, Luxembourg
Venue:
Computers in Industry
Year:
2007

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Abstract

This article presents the improvement of a defect recognition system for wooden boards by using knowledge integration from two expert fields. These two kinds of knowledge to integrate respectively concern wood expertise and industrial vision expertise. First of all, extraction, modelling and integration of knowledge use the Natural Language Information Analysis method (NIAM) to be formalized from their natural language expression. Then, to improve a classical industrial vision system , we propose to use the resulting symbolic model of knowledge to partially build a numeric model of wood defect recognition. This model is created according to a tree structure where each inference engine is a fuzzy rule based inference system. The expert knowledge model previously obtained is used to configure each node of the resulting hierarchical structure. The practical results we obtained in industrial conditions show the efficiency of such an approach.