On-line neural network-based stator fault diagnosis system of the converter-fed induction motor drive

This paper deals with the incipient stator-winding fault detection of the converter-fed induction motor drive. The fault level is modeled by change of a number of shorted stator-winding turns. The method based on a relative phase shift between the phase voltages and line currents of the converter-fed induction motor is used for the on-line fault monitoring and diagnosis. The fault indicators obtained for different load torque and supply frequency conditions for the drive system are used for neural network training. The on-line diagnosis system based on such neural detector is described and tested. Obtained experimental results show very good efficiency of the neural detector, which enables not only fault level evaluation (number of shorted turns) but also fault localization under drive system operation.