Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled FE-state space techniques

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. The dual-track of generating fault data and mining fault signatures was tested here on dynamic and static eccentricities of 10% and 30% percent of airgap height as well as cases of 1, 3, 6, and 9 broken bars and 3, 6, and 9 broken end-ring connectors. These cases were studied for proof-of-principle in a 208-volt, 60-Hz, 4-pole, 1.2-hp, squirrel cage 3-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 faults.

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