In induction machine a number of faults occur namely bearing and insulation related faults, stator winding and rotor related faults. Among these, stator inter-turn fault is one of the most common faults.Therefore, this work deals with the diagnosis of inter turn short circuit fault in stator winding of an induction machine. These incipient faults need to be identified and cleared as soon as possible to reduce failures as well as maintenance cost.Conventional methods are time taking and require exact mathematical modelling of the machine. However, due to ageing effects the mathematical model has to be modified from time to time so that one can employ soft computing methods which are suitable in the situation where dynamics of the system is less understood such as the fault dynamics of an induction machine. In this thesis, one of the very popular soft computing techniques called artificial neural network is employed to diagnose the stator inter turn short-circuit fault in a three phase squirrel cage induction machine. Firstly, a multilayer perceptron neural network (MLPNN) has been applied for solving the above fault diagnosis problem.The root mean square error was plotted and the least value was found to be 0.065. In view of improving the training performance, a radial basis function neural network (RBFNN) with the same configuration as that of back propagation algorithm and Discrete Wavelet Transform was designed. Then the results of both the artificial neural networks and DWT were compared and it was found that RBFNN outperforms both the MLPNN and DWT based fault diagnosis approaches applied to the induction machine.
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