Study of automobile engine fault diagnosis based on wavelet neural networks

Author

Weijie, Wang ; Yuanfu, Kang ; Xuezheng, Zhao ; Wentao, Huang

Author_Institution

Sch. of Mech. & Electr. Eng., Harbin Inst. of Technol., China

Volume

2

fYear

2004

fDate

15-19 June 2004

Firstpage

1766

Abstract

The engine vibration signals characters are extracted using wavelet packet technology. A model of wavelet neural networks is constructed based on wavelet frame theory and neural networks technology. Then multiresolution analysis is used to choose and optimize the wavelet neuron. The model is validated through the testing that simulates the faults of engine valve clearance. The experimental results show that the proposed automobile engine fault diagnostic model based on wavelet neural networks can diagnose the engine fault effectively.

Keywords

automotive components; fault diagnosis; internal combustion engines; mechanical engineering computing; neural nets; signal resolution; vibrations; wavelet transforms; automobile engine fault diagnosis; engine valve clearance; engine vibration signals characters; multiresolution analysis; neural networks technology; wavelet frame theory; wavelet neural networks; wavelet neuron; wavelet packet technology; Automobiles; Engines; Fault diagnosis; Multiresolution analysis; Neural networks; Neurons; Testing; Valves; Wavelet analysis; Wavelet packets;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1340976

Filename

1340976