Arabaci, HayriBilgin, Osman2020-03-262020-03-262012978-1-4673-0142-8https://hdl.handle.net/20.500.12395/2791320th International Conference on Electrical Machines (ICEM) -- SEP 02-05, 2012 -- Marseille, FRANCEThe paper presents squirrel cage induction motor rotor bar faults detection and classification by using stator current envelope at steady state operation. One of the stator currents has been used in the investigating of effects of rotor faults on the current envelopes. Fluctuations of the envelope were used as features of faults conditions for diagnosis. For feature extraction, frequency spectrum of the envelope of current was obtained by fast Fourier Transform (FFT). Significant picks in the spectrum were used to discern "healthy" and "faulty" motor conditions. The motor conditions were classified by Artificial Neural Network (ANN). In experiments three different rotor faults and healthy motor conditions were investigated by 30 HP, 8 '', with 18 bars, 380V, 2 poles and 50 Hz squirrel cage submersible induction motor. The proposed decision structure detects and classifies rotor bar faults with 100% accuracy.eninfo:eu-repo/semantics/closedAccessBroken rotor barfault diagnosisinduction motorsFast Fourier TransformArtificial Neural Networkstator current envelopeConference Object16431646N/AWOS:000333806701095N/A