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Analyse spectrale singulière des signaux vibratoires et Machine Learning pour la surveillance d'usure d'outils

Published online by Cambridge University Press:  17 May 2008

Bovic Kilundu  and
Pierre Dehombreux
Bovic Kilundu
Affiliation:
Service de génie mécanique, Faculté Polytechnique de Mons, 53 rue du Joncquois, 7000 Mons, Belgique
Pierre Dehombreux
Affiliation:
Service de génie mécanique, Faculté Polytechnique de Mons, 53 rue du Joncquois, 7000 Mons, Belgique
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Abstract

Cette étude explore l'utilisation des techniques de Machine Learning pour la classification de l'état d'outils en usinage. Une analyse spectrale singulière (ASS) pseudo-locale des signaux vibratoires relevés sur le porte-outil, couplée à un filtrage passe-bande a permis la définition et la mise en évidence d'indicateurs très sensibles à l'évolution de l'état de l'outil. Ces indicateurs sont définis à partir des sommes des raies spectrales des signaux reconstruits par ASS et de leurs résidus, dans des gammes de fréquence judicieusement choisies. Les taux de reconnaissance de l'usure sont très bons et dépassent les 80 %. Cette étude met en évidence deux aspects importants : la forte richesse en information des composantes hautes fréquences des signaux vibratoires et la possibilité de s'affranchir du bruit inutile par la combinaison de l'ASS et d'un filtrage passe-bande.

Keywords

Machine Learningvibration signalsingular spectrum analysistool condition monitoringMachine Learningsignaux vibratoiresanalyse spectrale singulièresurveillance de l'usure d'outils
Type
Research Article
Information
Mechanics & Industry , Volume 9 , Issue 1 , January 2008 , pp. 1 - 8
Copyright
© AFM, EDP Sciences, 2008

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