DocumentCode :
581361
Title :
On the use of stationary wavelet packet transform and multiclass wavelet SVM for broken rotor bar detection
Author :
Keskes, Hassen ; Braham, Ahmed ; Lachiri, Zied
Author_Institution :
Res. Lab., INSAT, Tunisia
fYear :
2012
fDate :
25-28 Oct. 2012
Firstpage :
3919
Lastpage :
3924
Abstract :
This paper proposes an original combination of Stationary Wavelet Packet Transform (SWPT) and Multiclass Wavelet Support Vector Machines (MWSVM) to detect broken rotor bar (BRB) in induction motor (IM). The SWPT is used for feature extraction under lower sampling rate. MWSVM is developed to perform the faults recognition. Different binary Multiclass SVM strategies are compared with various wavelet kernel functions in terms of classification accuracy, training and testing complexity. The experimental results show that the proposed method is able to detect the faulty conditions with high accuracy.
Keywords :
electric machine analysis computing; fault diagnosis; feature extraction; induction motors; rotors; support vector machines; wavelet transforms; IM; MWSVM; SWPT; binary multiclass SVM strategies; broken rotor bar detection; classification accuracy; faults recognition; feature extraction; induction motor; multiclass wavelet support vector machines; sampling rate; stationary wavelet packet transform; testing complexity; training; Accuracy; Amplitude modulation; Discrete wavelet transforms; Harmonic analysis; Fault detection; broken-rotorbar; induction motors; pattern recognition; support vector machines; wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society
Conference_Location :
Montreal, QC
ISSN :
1553-572X
Print_ISBN :
978-1-4673-2419-9
Electronic_ISBN :
1553-572X
Type :
conf
DOI :
10.1109/IECON.2012.6389266
Filename :
6389266
Link To Document :
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