Electric machines are the powerhouse of industrial plants and processes and play a very important role in their efficient and safe running. These machines operate under electrical, mechanical and thermal stresses making them prone to failing. Faults in the stator windings, due to a weak welding joint is one of the types of failures that can propagate and eventually lead to severe consequences. Timely detection of these types of faults is therefore crucial to avoid any damage to the machine. In this work, a framework has been put together for fault diagnosis, to detect and categorize a fault in the end turn windings of stators of PMAC and Induction motors. Feature extraction methods such as the Short Time Fourier Transform (STFT) and Wavelet Transform (WT) are implemented to extract the features by observing the energy densities. The features are categorized using classification methods like Nearest Neighbor Rule (NNR) and Linear Discriminant Analysis (LDA) to help classify the machine as either healthy or faulty, and identify the fault severity.
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