DocumentCode :
1597049
Title :
Online rotor bar breakage detection of three phase induction motors by wavelet packet decomposition and artificial neural network
Author :
Ye, Zhongming ; Wu, Bin
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, Ont., Canada
Volume :
4
fYear :
2001
fDate :
6/23/1905 12:00:00 AM
Firstpage :
2209
Abstract :
Online detection algorithm for induction motor rotor bar breakage is presented using a multi-layer perception network (MLP) and wavelet packet decomposition (WPD). New features of rotor bar faults are obtained by wavelet packet decomposition of the stator current. These features are of multiple frequency resolutions and obviously differentiate the healthy and faulty conditions. Features with different frequency resolutions are used together with the speed slip as the input sets of a 4-layer perceptron network. The algorithm is evaluated on a small three-phase induction motor with experiments. The laboratory results show that the proposed method is able to detect the faulty conditions with high accuracy. This algorithm is also applicable to the detection of other electrical faults of induction motors
Keywords :
automatic test software; fault diagnosis; machine testing; machine theory; multilayer perceptrons; power engineering computing; rotors; squirrel cage motors; stators; wavelet transforms; 4-layer perceptron network; artificial neural network; faulty conditions detection; multi-layer perception network; multiple frequency resolutions; online rotor bar breakage detection; rotor bar faults; speed slip; squirrel cage induction motor; stator current wavelet packet decomposition; three-phase induction motors; Circuit faults; Electrical fault detection; Fault detection; Frequency; Induction motors; Phase detection; Rotors; Stators; Synchronous motors; Wavelet packets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Electronics Specialists Conference, 2001. PESC. 2001 IEEE 32nd Annual
Conference_Location :
Vancouver, BC
ISSN :
0275-9306
Print_ISBN :
0-7803-7067-8
Type :
conf
DOI :
10.1109/PESC.2001.954448
Filename :
954448
Link To Document :
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