Arabaci H.Bilgin O.2020-03-262020-03-2620071424407192; 9781424407194https://dx.doi.org/10.1109/SIU.2007.4298628https://hdl.handle.net/20.500.12395/217802007 IEEE 15th Signal Processing and Communications Applications, SIU -- 11 June 2007 through 13 June 2007 -- Eskisehir -- 73089In this paper an experimental study detecting of rotor faults in three-phase squirrel cage induction motors by means of Short Time Fourier Transform (STFT) is presented. The frequency spectrum of motor line current is exploited for the detection. By obtaining a number of frequency spectrums from a current data with STFT and averaging these spectrums, faults are diagnosed instead of Fast Fourier Transform frequently applied at the detection of broken rotor faults in the literature. Five different faulted rotors are investigated. These faults are one bar with high resistance of the rotor, one broken bar of the rotor, two broken bars of the rotor, three broken bar of the rotor and broken end ring of the rotor. Artificial Neural Network is used for classification of faults. Test results show that this method increase the accuracy of the fault diagnose.tr10.1109/SIU.2007.4298628info:eu-repo/semantics/closedAccessConference ObjectN/A