Document Type




Format of Original

8 p.

Publication Date



Institute of Electrical and Electronics Engineers

Source Publication

IEEE Transactions on Energy Conversion

Source ISSN


Original Item ID

doi: 10.1109/60.986435


This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case data. Then, 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 three, six, and nine broken bar and broken end-ring connectors in a 208-volt, 60-Hz, 4-pole, 1.2-hp, squirrel cage 3-phase induction motor.


Accepted version. IEEE Transactions on Energy Conversion, Vol. 17, No. 1 (March 2002): 39-46. DOI. © 2002 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

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