Intelligent Diagnosis Techniques in Automotive Engines Fault Based on Fuzzy Support Vector Machine

Author

Weng, Maorong

Author_Institution

Dept. of Mech. & Automotive Eng., Zhejiang Ind. & Trade Vocational Coll., Wenzhou, China

fYear

2010

fDate

17-18 April 2010

Firstpage

40

Lastpage

43

Abstract

The fault reasoning and diagnosis are carried out by means of analysis on the contents of CO, HC, CO₂ and O₂ in the exhaust gas discharged from gasoline engine. The support vector machine is applied to fault diagnosis of the exhaust gas discharged from gasoline engine. Taken the fault data recorded the exhaust gas discharged from gasoline engine as the sample sets, part of the fault data of the exhaust gas is studied, by use of classification machine. The calculation results show that the average accuracy of the fault diagnosis could reach about 95% with fuzzy support vector machine.

Keywords

fault diagnosis; fuzzy set theory; support vector machines; automotive engines fault; fault diagnosis; fault reasoning; fuzzy support vector machine; gasoline engine; intelligent diagnosis techniques; Automotive engineering; Carbon dioxide; Engines; Fault diagnosis; Fuzzy systems; Intelligent vehicles; Machine intelligence; Petroleum; Support vector machine classification; Support vector machines; fault diagnosis; fuzzy support vector machine; gasoline engine;

fLanguage

English

Publisher

ieee

Conference_Titel

Wearable Computing Systems (APWCS), 2010 Asia-Pacific Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-6467-8

Electronic_ISBN

978-1-4244-6468-5

Type

conf

DOI

10.1109/APWCS.2010.17

Filename

5481110