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