Induction motors are one of the commonly used electrical machines in industry because of various technical and economical reasons. These machines face various stresses during operating conditions. These stresses might lead to some modes of failures/faults. Hence condition monitoring becomes necessary in order to avoid catastrophic faults. Various fault monitoring techniques for induction motors can be broadly categorized as model based techniques, signal processing techniques, and soft computing techniques. In case of model based techniques, accurate models of the faulty machine are essentially required for achieving a good fault diagnosis. Sometimes it becomes difficult to obtain accurate models of the faulty machines and also to apply model based techniques. Soft computing techniques provide good analysis of a faulty system even if accurate models are unavailable. Besides giving improved performance these techniques are easy to extend and modify. These can be made adaptive by the incorporation of new data or information. Multilayer perceptron neural network using back propagation algorithm have been extensively applied earlier for the detection of an inter-turn short circuit fault in the stator winding of an induction motor. This thesis extends applying other neuro-computing paradigms such as recurrent neural network (RNN), radial basis function neural network (RBFNN), and adaptive neural fuzzy inference system (ANFIS) for the detection and location of an inter-turn short circuit fault in the stator winding of an induction motor. By using the neural networks, one can identify the particular phase of the induction motor where the inter-turn short circuit fault occurs. Subsequently, a discrete wavelet technique is exploited not only for the detection and location of an inter-turn short circuit fault but also to find out the quantification of degree of this fault in the stator winding of an induction motor. In this work, we have developed an experimental setup for the calculation of induction motor parameters under both healthy and inter-turn short circuit faulty conditions. These parameters are used to generate the phase shifts between the line currents and phase voltages under different load conditions. The detection and location of an inter-turn short circuit fault in the stator winding is based on the monitoring of these three phase shifts. Extensive simulation results are presented in this thesis to demonstrate the effectiveness of the proposed methods
