Feature Extraction and SOM for Bearing Fault Diagnosis
Type : Article de conférence
Auteur(s) : , , , ,
Année : 2014
Domaine : Automatique
Conférence: The 4th International Conference on Welding, Non Destructive Testing and Materials and Alloys Industry (IC-WNDT-MI’14)
Lieu de la conférence: Annaba, Algeria
Résumé en PDF :
Fulltext en PDF :
Mots clés : Condition monitoring, Discrete wavelet transform, Fault Diagnosis, Kurtosis, Roller Bearing, Rotating machines, Self-organization Map, Vibration measurement.
Auteur(s) : , , , ,
Année : 2014
Domaine : Automatique
Conférence: The 4th International Conference on Welding, Non Destructive Testing and Materials and Alloys Industry (IC-WNDT-MI’14)
Lieu de la conférence: Annaba, Algeria
Résumé en PDF :
Fulltext en 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.