Fault Diagnosis of Diesel Fuel Ejection System Based on Improved WNN

Author

Shen, Yanqing ; Cao, Longhan ; Wang, Zhujing ; Zhou, Shanquan ; Gou, Bingyong

Author_Institution

Control Eng. Lab, Chongqing Commun. Inst.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5752

Lastpage

5755

Abstract

To remedy the disadvantages of conventional diesel engine fuel ejection system´s fault diagnosis method, which couldn´t get exact results and dispel noise effectively, a new way based on wavelet neural network (WNN) which combines merits of wavelet transform (WT) and RBF neural network (RBFNN) was put forward. Moreover, after picking up fault characteristic parameters, we trained it with genetic algorithm (GA) and simulated annealing (SA). Finally we applied the improved WNN to fault diagnosis of diesel engine fuel ejection system. The results show that the algorithm is good at dispelling noise, stable, and effective in high precise fault diagnosis

Keywords

automotive engineering; diesel engines; fault diagnosis; fuel systems; genetic algorithms; neural nets; simulated annealing; wavelet transforms; RBF neural network; diesel engine fuel ejection system; fault diagnosis method; genetic algorithm; simulated annealing; wavelet neural network; wavelet transform; Artificial neural networks; Continuous wavelet transforms; Control engineering; Diesel engines; Fault diagnosis; Fuels; Genetic algorithms; Neural networks; Simulated annealing; Wavelet transforms; Diesel Engine; Fault Diagnosis; Genetic Algorithm; Simulated Annealing; Wavelet Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714177

Filename

1714177