Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine

Résumé

This paper discusses the fault features selection using principal component analysis and using multi-class support vector machine (MSVM) for bearing faults classification. The bearings vibration signal is obtained from experiment in accordance with the following conditions: normal bearing, bearing with inner race fault, bearing with outer race fault and bearings with balls fault. Statistical parameters of vibration signal such as mean, standard deviation, sample variance, kurtosis, skewness, etc, are processed with principal component analysis (PCA) for extracting the optimal features and reducing the dimension of original features. The multi-class classification algorithm of support vector machine (SVM), one against one strategy, is used for bearing multi-class fault diagnosis. The performance of the method proposed was high accurate and efficient.
Fichier principal
Vignette du fichier
978-3-642-18369-0_22_Chapter.pdf (431.96 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01564862 , version 1 (19-07-2017)

Licence

Paternité

Identifiants

Citer

Guifeng Jia, Shengfa Yuan, Chengwen Tang. Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.198-205, ⟨10.1007/978-3-642-18369-0_22⟩. ⟨hal-01564862⟩
59 Consultations
86 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More