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