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. 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. Index Terms--Artificial intelligence, data mining, diagnostics through current waveforms, dynamical systems analysis, electric drives, fault diagnosis, induction motors, state-space methods, time series, time-stepping finite elements.