Online monitoring of induction motor health is of increasing interest, as the industrial processes that depend on these motors become more complex and as the performance to cost ratio of monitoring technology (e.g. sensors, microprocessors) continues to increase. Much effort has been directed towards developing methods that use conventional signal processing and pattern classification techniques. This text addresses the main issues of detecting electrical and mechanical faults using the information provided by current and vibration sensors, within a probabilistic framework. The faults studied in this work are both electrical and mechanical. The framework developed provides a common solution methodology for the detection of all these different faults. The methodology utilizes a combination of machine modeling concepts, along with wavelet, and symbolic dynamic analysis to ensure early detection. Additionally a sensor fusion technique is also developed to merge information from the current and vibration sensors.

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