Vibration signal-based bearing fault diagnosis usingoptimized multi-scale entropy and ANFIS network

Type : Article de conférence
Auteur(s) :  N. Fergani, N. BOUTASSETA, B. Oudjani, A. Deliou, I. Attoui
Année :  2014
Domaine : Automatique
Conférence: International Conference on Welding, Non Destructive Testing and Materials and Alloys Industry
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  Fault Diagnosis, Vibration analysis, multi-scale entropy (MSE), bearing faults

Résumé : 

This paper presents an application of a multi-scaleclassification method to detect bearing-related faults in anexperimental benchmark. Multi-scale analysis of the vibrationsignal allows the representation of nonlinear dynamics andcoupling effects between different mechanical components ofindustrial equipment. An improved multi-scale entropy analysisis used as features extraction tool for the diagnosis procedure.The classification of the state of health of the bearings is achievedusing adaptive neuro-fuzzy inference system and neural networksfor different faults scenarios with variable fault severity.Experimental results show the importance of the choice of thefeatures extraction method for the classification of faults and thedetermination of their severity.