Bearing Fault Diagnosis based on Independent Component Analysis and Optimized Support Vector Machine

被引：0
|
作者：
Thelaidjia, Tawfik [1 ,2 ]
Moussaoui, Abdelkrim [1 ]
Chenikher, Salah [3 ]
机构：
[1] Univ Guelma, Lab Elect Engn Guelma, Guelma, Algeria
[2] Res Ctr Ind Technol CRTI, Algiers 16014, Algeria
[3] Tebessa Univ, LABGET, Lab Elect Engn, Tebessa, Algeria
来源：
2015 7TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC) | 2014年
关键词：
Independent Component Analysis; Fault Diagnosis; Particle swarm optimization; Roller Bearing; Statistical parameters; Support Vector Machine; Discrete Wavelet Transform;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This study concerns with fault diagnosis in rolling bearings using discrete wavelet transform (DWT), statistical parameters, independent component analysis (ICA) and support vector machine (SVM). The features for classification are extracted through using statistical parameters combined with energy obtained through the application of Db2-discrete wavelet transform at the fifth level of decomposition. After feature extraction, ICA is employed to select the relevant features. Finally an optimized SVM based on particle swarm optimization (PSO) is used for bearing fault decision. The obtained results proved the effectiveness of the proposed methodology for bearing faults diagnosis.
引用
收藏
页码：160 / 163
页数：4
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