Title :
Fault Monitoring and Diagnosis of Induction Machines Based on Harmonic Wavelet Transform and Wavelet Neural Network
Author :
Guo, Qianjin ; Li, Xiaoli ; Yu, Haibin ; Che, Xiangzhi ; Hu, Wei ; Hu, Jingtao
Author_Institution :
Shenyang Inst. of Autom., Chinese Acad. of Sci., Shenyang
Abstract :
The fault symptoms of stator winding inter-turn short circuit and rotor bar breakage are analyzed completely in this paper. And a new method for fault diagnosis of broken rotor bar and inter-turn short-circuits in induction machines is presented. The method is based on the analysis of the motor current signature analysis of induction machines using Zoom FFT spectrum analysis, generalized harmonic wavelet transform filter and hybrid particle swarm optimization (HPSO) based wavelet neural network. As an on-line current monitoring and non-invasive detection scheme, the presented method yields a high degree of accuracy in fault identification as evidenced by the given experimental results, which demonstrate that the detection scheme is valid and feasible.
Keywords :
asynchronous machines; computerised monitoring; electric machine analysis computing; fast Fourier transforms; fault diagnosis; neural nets; particle swarm optimisation; rotors; short-circuit currents; stators; wavelet transforms; fault diagnosis; fault identification; fault monitoring; generalized harmonic wavelet transform filter; harmonic wavelet transform; hybrid particle swarm optimization; induction machines; motor current signature analysis; noninvasive detection scheme; rotor bar breakage; stator winding inter-turn short circuit; wavelet neural network; zoom FFT spectrum analysis; Circuit faults; Condition monitoring; Fault diagnosis; Harmonic analysis; Induction machines; Neural networks; Power harmonic filters; Rotors; Wavelet analysis; Wavelet transforms; Fault Diagnosis; Harmonic Wavelet Transform; Induction Machines; Wavelet neural Network;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.663