M. Ebrahimi, M. Moradiyan, H. Moeshginkelk, M. Danesh, and M. Bayat,
Volume 25, Issue 1 (7-2006)
Abstract
This paper presents a method based on neural networks to detect broken rotor bars and end rings in squirrel cage induction motors. In the first part, detection methods are reviewed and traditional methods of fault detection as well as dynamic
model of induction motors are introduced using the winding function method. In this method, all stator and rotor bars are considered independently in order to check the performance of the motor for any faults in the parts. Then the frequency spectrum of current signals is derived using the Fourier transform and analyzed under various conditions. In the second part of the paper, an analytical discussion of the theoretical principles is presented to arrive at a simple algorithm for fault detection based on neural networks. The neural network has been trained using the information from a 1.1 KW induction motor. Finally, the system is tested with different values of load torque and is found capable of working on-line to detect all normal and ill-performing conditions.
