Title :
Intelligent Diagnosis Techniques in Automotive Engines Fault Based on Fuzzy Support Vector Machine
Author_Institution :
Dept. of Mech. & Automotive Eng., Zhejiang Ind. & Trade Vocational Coll., Wenzhou, China
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;
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
DOI :
10.1109/APWCS.2010.17