Abstract
Progress in field of electrical machines diagnosis and artificial intelligence contributed to increased interest in new methods of diagnosis in field of electrical motors. The paper focused on three numerical techniques: finite element analysis, signal analysis and artificial neural networks in diagnosis of a squirrel cage induction machine under inter-turn short circuit in stator windings. A field-circuit model of machine was used to calculate waveforms of torque for a selected number of shorted turns. The obtained waveforms were analysed by wavelet transform. Results of analysis were used as input vector of artificial neural network. An output vector of artificial neural network was a number of shorted turns of stator winding. In this paper generalized regression neural network (GRNN) were compared with multi layer perceptron neural network (MLP).
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