DocumentCode :
3638944
Title :
Artificial neural networks broken rotor bars induction motor fault detection
Author :
Dragan Matic;Filip Kulic;Vincente Climente-Alarcon;Ruben Puche-Panadero
Author_Institution :
Faculty of Technical Science, Department for, Automation and System Control, Trg Dositeja Obradovi_a 6, 21000 Novi, Sad
fYear :
2010
Firstpage :
49
Lastpage :
53
Abstract :
Paper deals with application of online rotor broken bar fault detection via artificial neural networks. Fault can be detected by monitoring abnormalities of the spectrum amplitudes at certain frequencies in the motor current spectrum. These discriminative features are used for training of feed-forward backpropagation artificial neural network. Trained network is capable to successfully classify induction motor rotor condition. Results are presented in tables and figures.
Keywords :
"Rotors","Induction motors","Artificial neural networks","Bars","Amplitude modulation","Fault detection","Classification algorithms"
Publisher :
ieee
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2010 10th Symposium on
Print_ISBN :
978-1-4244-8821-6
Type :
conf
DOI :
10.1109/NEUREL.2010.5644051
Filename :
5644051
Link To Document :
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