DocumentCode :
1339048
Title :
Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networks
Author :
Chow, Mo-Yuen ; Yee, Sui Oi
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
Volume :
6
Issue :
3
fYear :
1991
fDate :
9/1/1991 12:00:00 AM
Firstpage :
536
Lastpage :
545
Abstract :
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
Keywords :
electric machine analysis computing; electrical faults; neural nets; squirrel cage motors; artificial neural networks; disturbance filter; noise filter; on-line incipient fault detection; single-phase squirrel-cage induction motors; steady-state measurements; transient measurements; Artificial neural networks; Circuit faults; Electrical fault detection; Fault detection; Filters; Induction motors; Noise measurement; Protection; Prototypes; Rotating machines;
fLanguage :
English
Journal_Title :
Energy Conversion, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8969
Type :
jour
DOI :
10.1109/60.84332
Filename :
84332
Link To Document :
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