DocumentCode :
2753515
Title :
A Novel Method of Intelligent Fault Diagnosis for Diesel Engine
Author :
Zhang, Xu ; Sun, Jianbo ; GUO, Chen
Author_Institution :
Autom. & Electr. Eng., Dalian Maritime Univ.
Volume :
2
fYear :
0
fDate :
0-0 0
Firstpage :
5739
Lastpage :
5743
Abstract :
On the point view of complementary strategies, a new hybrid algorithm to optimize the RBF network based on artificial immunology was proposed. A dynamic clustering algorithm based on clonal selection algorithm was used to specify the amount and initial position of the RBF centers; then RBF network was trained by the immune evolutionary algorithm. Combining with the rough sets-based attribute reduction algorithm, a novel hybrid system of rough sets and immune-RBF network for intelligent fault diagnosis were put forward. The diagnosis of diesel demonstrates that the method can effectively simplify the structure of network, and increase the efficiency and precision of diagnosis
Keywords :
automotive engineering; diesel engines; evolutionary computation; fault diagnosis; pattern clustering; radial basis function networks; rough set theory; RBF neural network; artificial immunology; clonal selection algorithm; diesel engine; dynamic clustering algorithm; immune evolutionary algorithm; intelligent fault diagnosis; rough sets-based attribute reduction algorithm; Artificial intelligence; Automation; Clustering algorithms; Diesel engines; Electronic mail; Fault diagnosis; Heuristic algorithms; Radial basis function networks; Rough sets; Sun; RBF neural networks; artificial immune; fault diagnosis; rough sets;
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.2006.1714174
Filename :
1714174
Link To Document :
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