DocumentCode :
553874
Title :
Fault diagnosis of engine misfire based on genetic optimized support vector machine
Author :
Di Lu ; Wenjuan Dou
Author_Institution :
Coll. of Electr. & Electron. Eng., Harbin Univ. of Sci. & Technol., Harbin, China
Volume :
1
fYear :
2011
fDate :
22-24 Aug. 2011
Firstpage :
250
Lastpage :
253
Abstract :
In this paper an intelligent algorithm for commen misfire fault of automobile engines is proposed, in which the support vector machine(SVM) is used to extract the volume fractions of the automobile emission and to improve the accuracy of fault diagnosis, the genetic algorithms(GA) is adopted to optimize the parameters of SVM algorithm. Simulation results demonstrate GA-SVM algorithm can obtain satisfied classification result, the diagnosis speed and accuracy by GA-SVM algorithm are better than traditional SVM algorithm. The result shows that the GA-SVM algorithm has a very high accuracy for small sample fault diagnosis, thus the proposed algorithm is suitable for mechanical fault diagnosis of misfire fault of automobile engines.
Keywords :
fault diagnosis; genetic algorithms; internal combustion engines; mechanical engineering computing; support vector machines; GA-SVM algorithm; automobile emission; automobile engine misfire fault; genetic algorithm; genetic optimized support vector machine; intelligent algorithm; mechanical fault diagnosis; satisfied classification result; volume fraction extraction; Agriculture; Classification algorithms; Lead; Support vector machines; Testing; Fault diagnosis; Genetic algorithms; Misfire; Support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Strategic Technology (IFOST), 2011 6th International Forum on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-4577-0398-0
Type :
conf
DOI :
10.1109/IFOST.2011.6021015
Filename :
6021015
Link To Document :
بازگشت