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Feature Extraction and SOM for Bearing Fault Diagnosis

Auteurs : Tawfik THELAIDJIA, Abdelkrim Moussaoui, Salah CHENIKHER, Amar BOUTAGHANE, Sami KAHLA
Année : 2014
Domaine : Automatique
Type : Communication
Conférence: International Conference on NDT and Material Industry and Alloys (IC-WNDT-MI'14).
Résumé en PDF : (résumé en pdf)
Fulltext en PDF : (.pdf)
Mots clés : Condition monitoring, Discrete wavelet transform, Fault Diagnosis, Kurtosis, Roller Bearing, Rotating machines, Self-organization Map, Vibration measurement.

Résumé :

In this paper a method for fault diagnosis of rolling bearings is presented. It consists of two parts: vibration signal feature extraction and condition classification. The aim of the first step is the extraction of the relevant parameters; the proposed technique consists of preprocessing the bearing fault vibration signal using a combination of the signal’s Kurtosis and discrete wavelet transform (DWT). The Self-organization Map (SOM) is used to accomplish the classification step and automate the fault diagnosis procedure. The results have shown feasibility and effectiveness of the proposed approach.