Classification and Diagnosis of Broken Rotor Bar Faults in Induction Motor using Spectral Analysis and SVM

被引:0
|
作者
Amel, Bouchemha [1 ]
Laatra, Yousfi [1 ]
Sami, Sakhri [1 ]
Nourreddine, Doghmane
机构
[1] Univ Tebessa, Dept Elect Engn, Tebessa, Algeria
来源
2013 8TH INTERNATIONAL CONFERENCE AND EXHIBITION ON ECOLOGICAL VEHICLES AND RENEWABLE ENERGIES (EVER) | 2013年
关键词
Broken rotor bars; Fault diagnosis; Motor current spectral analysis; Support Vector machine; MACHINES;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In this paper, we propose to detect and localize the broken bar faults in multi-winding induction motor using Motor current signature (MCSA) combined to Support Vector Machine (SVM). The analysis of stator currents in the frequency domain is the most commonly used method, because induction machine faults often generates particular frequency components in the stator current spectrum. In order to obtain a more robust diagnosis, we propose to classify the feature vectors extracted from the magnitude of spectral analysis using multi-class SVM to discriminate the state of the motor. Finally, in order to validate our proposed approach, we simulated the multi-winding induction motor under Matlab software. Promising results were obtained, which confirms the validity of the proposed approach.
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页数:5
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