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