Wavelet neural network method for fault diagnosis of push-pull circuits

Author

Luo, Zhi-Yong ; Shi, Zhong-ke

Author_Institution

Coll. of Autom., Northwestern Polytech. Univ., Xi´´an, China

Volume

6

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

3327

Abstract

A wavelet neural network method for fault diagnosis of push-pull circuits is presented. Firstly, output voltage signals under faulty conditions are obtained with simulation. Then wavelet coefficients of output voltage signals are gained by Daubechies wavelet decomposition, and faulty feature vectors are extracted from coefficients. After training the networks by faulty feature vectors, the wavelet neural networks model of the circuit fault diagnosis system is built. The simulation result shows the fault diagnosis method of the push-pull circuits with wavelet neural network is effective.

Keywords

circuit simulation; fault diagnosis; feature extraction; learning (artificial intelligence); neural nets; wavelet transforms; Daubechies wavelet decomposition; circuit simulation; fault diagnosis; faulty feature vector extraction; neural net training; output voltage signal; push-pull circuit; wavelet neural network; wavelet transform; Circuit faults; Circuit simulation; Fault diagnosis; Neural networks; Power system modeling; Pulse transformers; Pulse width modulation; Space vector pulse width modulation; Voltage; Wavelet coefficients; Fault diagnosis; push-pull circuits; simulation; wavelet neural networks; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527517

Filename

1527517