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
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"
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2010 10th Symposium on
Print_ISBN :
978-1-4244-8821-6
DOI :
10.1109/NEUREL.2010.5644051