Title :
Induction motor mechanical fault online diagnosis with the application of artificial neural network
Author :
Ye, Zhongming ; Wu, Bin ; Sadeghian, A.R.
Author_Institution :
Dept. of Electr. Eng., Ryerson Polytech. Inst., Toronto, Ont., Canada
Abstract :
An online fault diagnostic algorithm for induction motor mechanical faults is presented based on the application of artificial neural networks. Two mechanical faults, the rotor bar breakage and air gap eccentricity, are considered. New feature coefficients obtained by wavelet packet decomposition of the stator current are used together with the slip speed as the input of a multi-layer neural network. The proposed algorithm is proved to be able to distinguish healthy and faulty conditions with high accuracy
Keywords :
air gaps; electric machine analysis computing; fault diagnosis; induction motors; machine testing; neural nets; rotors; slip (asynchronous machines); stators; wavelet transforms; air gap eccentricity; artificial neural network; artificial neural networks; faulty conditions; feature coefficients; healthy conditions; induction motor; mechanical fault online diagnosis; multi-layer neural network; rotor bar breakage; slip speed; stator current; wavelet packet decomposition; Application software; Artificial neural networks; Chemical analysis; Condition monitoring; Fault diagnosis; Induction motors; Power harmonic filters; Rotors; Stators; Vibration measurement;
Conference_Titel :
Applied Power Electronics Conference and Exposition, 2001. APEC 2001. Sixteenth Annual IEEE
Conference_Location :
Anaheim, CA
Print_ISBN :
0-7803-6618-2
DOI :
10.1109/APEC.2001.912491
