DocumentCode :
1902199
Title :
Diagnosis of stator winding inter-turn short circuit in three-phase induction motors by using artificial neural networks
Author :
Broniera, P.J. ; Gongora, W.S. ; Goedtel, A. ; Godoy, W.F.
Author_Institution :
Dept. of Electr. Eng., UTFPR Univ., Cornelio Procopio, Brazil
fYear :
2013
fDate :
27-30 Aug. 2013
Firstpage :
281
Lastpage :
287
Abstract :
The application of induction motors in industry is widespread. Thus, several studies have presented strategies for the diagnosis and prediction of failures in these motors. One technique used is based on the recent utilization of intelligent systems for detecting faults in electric motors. Thus, this paper proposes an alternative tool to traditional techniques for fault detection of a short circuit between the inter-turns of the stator winding using artificial neural networks to analyze stator current signals in the time domain. Experimental results are presented to validate the proposed approach.
Keywords :
fault diagnosis; induction motors; neural nets; power engineering computing; stators; time-domain analysis; artificial neural networks; fault detection; stator current signals; stator winding interturn short circuit diagnosis; three-phase induction motors; time domain analysis; Artificial neural networks; Circuit faults; Induction motors; Neurons; Stator windings; Training; Artificial Neural Networks; Stator faults; Three phase induction motors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on
Conference_Location :
Valencia
Type :
conf
DOI :
10.1109/DEMPED.2013.6645729
Filename :
6645729
Link To Document :
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