Title :
The Detection of Rotor Faults Using Artificial Neural Network
Author :
Arabaci, Hayri ; Bilgin, Osman
Author_Institution :
Elektrik ve Elektron. Muhendisligi Bolumu, Selcuk Univ., Konya
Abstract :
The detection of broken rotor bars in three-phase squirrel cage induction motors by means of current signature analysis is presented. In order to diagnose faults, a neural network approach is used. At first the data of different rotor faults are achieved. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated via calculating power spectrum density (PSD). Training the neural network discern between "healthy" and "faulty" motor conditions by using experimental data in case of healthy and faulted motor. The test results clearly illustrate that the stator current signature can be used to diagnose faults of squirrel cage rotor
Keywords :
electric machine analysis computing; fault diagnosis; learning (artificial intelligence); neural nets; rotors; spectral analysis; squirrel cage motors; stators; PSD; artificial neural network; current signature analysis; power spectrum density; rotor faults detection; three-phase squirrel cage induction motor; Artificial neural networks; Bars; Fast Fourier transforms; Fault detection; Induction motors; Neural networks; Rotors; Stators; Testing;
Conference_Titel :
Signal Processing and Communications Applications, 2006 IEEE 14th
Conference_Location :
Antalya
Print_ISBN :
1-4244-0238-7
DOI :
10.1109/SIU.2006.1659690
