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Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networks

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2 Author(s)
Mo-Yuen Chow ; Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA ; Yee, S.O.

A novel approach for online detection of incipient faults in single-phase squirrel-cage induction motors through the use of artificial neural networks is presented. The online incipient fault detector is composed of two parts: (1) a disturbance and noise filter artificial neural network to filter out the transient measurements while retaining the steady-state measurements, and (2) a high-order incipient fault detection artificial neural network to detect incipient faults in single-phase squirrel-cage induction motors based on data collected from the motor. Simulation results show that neural networks yield satisfactory performance for online detection of incipient faults in single-phase squirrel-cage induction motors. The neural network fault detection methodology presented is not limited to single-phase squirrel-cage motors (used as a prototype), but can also be applied to many other types of rotating machines, with the appropriate modifications

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Energy Conversion, IEEE Transactions on  (Volume:6 ,  Issue: 3 )