DocumentCode :
3649183
Title :
Diagnosis of broken rotor bar faults by using frequency spectrum of stator current envelope
Author :
Hayri Arabacı;Osman Bilgin
Author_Institution :
Department of Electrical and Electronics Engineering, Selcuk University, 42075, Konya, Turkey
fYear :
2012
Firstpage :
1643
Lastpage :
1646
Abstract :
The paper presents squirrel cage induction motor rotor bar faults detection and classification by using stator current envelope at steady state operation. One of the stator currents has been used in the investigating of effects of rotor faults on the current envelopes. Fluctuations of the envelope were used as features of faults conditions for diagnosis. For feature extraction, frequency spectrum of the envelope of current was obtained by fast Fourier Transform (FFT). Significant picks in the spectrum were used to discern “healthy” and “faulty” motor conditions. The motor conditions were classified by Artificial Neural Network (ANN). In experiments three different rotor faults and healthy motor conditions were investigated by 30 HP, 8”, with 18 bars, 380V, 2 poles and 50 Hz squirrel cage submersible induction motor. The proposed decision structure detects and classifies rotor bar faults with 100% accuracy.
Keywords :
"Rotors","Induction motors","Bars","Stators","Artificial neural networks","Training","Fluctuations"
Publisher :
ieee
Conference_Titel :
Electrical Machines (ICEM), 2012 XXth International Conference on
Print_ISBN :
978-1-4673-0143-5
Type :
conf
DOI :
10.1109/ICElMach.2012.6350100
Filename :
6350100
Link To Document :
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