Induction machine bearing faults detection based on artificial neural network

Electrical machines are frequently facing bearing faults due to fatigue or wear. The detection of any damages in their incipient phase can contribute to prevention of unplanned breakdowns in industrial environment. In this paper an artificial neural network (ANN) based bearing fault detection method is detailed. Upon this method the phase currents of the induction machines are measured and analyzed by means of a new classifier scheme laying on a flexible ANN and an optimal smoothed graphical representation. For both the healthy and faulty machines specific kernels were identified. The results obtained by using the proposed classifier show that the applied Levenberg-Marquardt algorithm for the ANN training is an excellent choice for such diagnosis purposes and it can be a beneficial method for all electrical machine diagnosticians.