DocumentCode :
2024120
Title :
Online mechanical fault diagnosis of induction motor by wavelet artificial neural network using stator current
Author :
Ye, Zhongming ; Bin Wu ; Zargari, Navid
Author_Institution :
Dept. of Electr. Eng., Ryerson Polytech. Univ., Toronto, Ont., Canada
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
1183
Abstract :
A novel online fault diagnostic method for the mechanical faults of induction motors is proposed. The method is based on artificial neural networks and Wavelet Packet Decomposition. New feature for mechanical fault detection is defined through the signature analysis of the wavelet packet decomposition coefficients of induction motor stater current. The theoretical background and description of the detection algorithm using artificial neural network is presented. Simulation results prove that the proposed method accurately detects the faults for a wide range of load conditions
Keywords :
fault diagnosis; induction motors; mechanical engineering computing; neural nets; wavelet transforms; artificial neural networks; fault diagnostic method; induction motors; wavelet packet decomposition; Artificial neural networks; Fault diagnosis; Frequency; Induction motors; Neural networks; Rotors; Stators; Thermal stresses; Wavelet analysis; Wavelet packets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
Type :
conf
DOI :
10.1109/IECON.2000.972290
Filename :
972290
Link To Document :
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