DocumentCode :
2895134
Title :
Applied Study of Electromotor Fault Diagnosis Based on Wavelet Packets and Neural Network
Author :
Hou, Bei-ping ; Zhu, Wen ; Xiang, Xin-jian ; Shang, Xing-yao
Author_Institution :
Sch. of Autom. & Electr., Zhejiang Univ. of Sci. & Technol., Hangzhou
fYear :
2006
fDate :
13-16 Aug. 2006
Firstpage :
2912
Lastpage :
2916
Abstract :
This paper presents a novelty electromotor fault diagnosis method based on wavelet packets and BP neural network. Wavelet packets transform can decompose original signal into different spectrums, the corresponding energy eigenvector can be obtained, it expresses the energy characteristics of original signal; BP (back propagation) neural network is an effective tool to recognize fault types, neural network can be trained by sample signal, then it can recognize practical fault types. The practical example indicates the validity of the new method
Keywords :
backpropagation; electric machine analysis computing; electric motors; fault diagnosis; neural nets; signal sampling; wavelet transforms; backpropagation neural network; electromotor fault diagnosis; neural network; signal sampling; wavelet packet transform; Character recognition; Cybernetics; Energy resolution; Fault diagnosis; Frequency domain analysis; Karhunen-Loeve transforms; Machine learning; Neural networks; Signal resolution; Vibration measurement; Wavelet analysis; Wavelet packets; Wavelet transforms; Zinc; BP neural network; Wavelet packets; fault diagnosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
Type :
conf
DOI :
10.1109/ICMLC.2006.259080
Filename :
4028559
Link To Document :
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