T. Yektaniroumand - Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
M. Niaz Azari - Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
M. Gholami - Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

This study examined and presents an effective method for detection of failure of conductor bars in the winding of rotor of induction motor in low load conditions using neural networks of radial-base functions. The proposed method used Hilbert method to obtain the stator current signal push. The frequency and signal amplitude of the push stator were used as the input of the neural network and the network outputs were rotor fault state, and the number of conductive bars with broken fault. Moreover, particle-swarm optimization algorithm was used to determine the optimal network weights and neuron penetration radius in the neural network. The results obtained from the proposed method showed the optimal and efficient performance of the method in detecting conductive bars broken fault in induction motor in low load conditions.

fault detection, Induction motor, Hilbert transform, Neural Network, Particle-Swarm Optimization