NDLI: Fault Diagnosis of Diesel Fuel Ejection System Based on Improved WNN

Content Provider	IEEE Xplore Digital Library
Author	Yanqing Shen Longhan Cao ZhujingWang ShanquanZhou Bingyong Gou
Copyright Year	2006
Description	Author affiliation: Control Eng. Lab, Chongqing Commun. Inst. (Yanqing Shen; Longhan Cao; ZhujingWang; ShanquanZhou; Bingyong Gou)
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
Starting Page	5752
Ending Page	5755
File Size	123564
Page Count	4
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Fuels Wavelet transforms Neural networks Continuous wavelet transforms Diesel engines Artificial neural networks Control engineering Genetic algorithms Simulated annealing Wavelet Neural Network Diesel Engine Fault Diagnosis Genetic Algorithm Simulated Annealing
Content Type	Text
Resource Type	Article

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