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This paper develops the foundations of a technique for detection and categorization of dynamic/static eccentricities and bar/end-ring connector breakages in squirrel-cage induction motors that is not based on the traditional Fourier transform frequency-domain spectral analysis concepts. Hence, this approach can distinguish between the "fault signatures" of each of the following faults: eccentricities, broken bars, and broken end-ring connectors in such induction motors. Furthermore, the techniques presented here can extensively and economically predict and characterize faults from the induction machine adjustable-speed drive design data without the need to have had actual fault data from field experience. This is done through the development of dual-track studies of fault simulations and, hence, simulated fault signature data. These studies are performed using our proven time-stepping coupled finite-element-state-space method to generate fault case performance data, which contain phase current waveforms and time-domain torque profiles. Then, from this data, the fault cases are classified by their inherent characteristics, so-called "signatures" or "fingerprints." These fault signatures are extracted or "mined" here from the fault case data using our novel time-series data mining technique. The dual track of generating fault data and mining fault signatures was tested here on dynamic and static eccentricities of 10% and 30% of air-gap height as well as cases of one, three, six, and nine broken bars and three, six, and nine broken end-ring connectors. These cases were studied for proof of principle in a 208 V 60 Hz four-pole 1.2 hp squirrel-cage three-phase induction motor. The paper presents faulty and healthy performance characteristics and their corresponding so-called phase space diagnoses that show distinct fault signatures of each of the above-mentioned motor faults.