Title :
Study of automobile electric controlled system fault diagnosis based on classification pattern recognition
Author :
Zhang Lili ; Chu Jiangwei ; Zou Bencun
Author_Institution :
Dept. of Traffic Coll., Northeast Forestry Univ., Harbin, China
Abstract :
The analysis of data flow from electric controlled system of On-Board Diagnosis shows that sensor and actuator fault could be separated obviously, and then use Neural Network and D-S evidence information fusing method for final recognition. The result shows that, the method based on classification fault recognition could not only promote the precision, but also could promote the efficiency on some degree.
Keywords :
actuators; automobiles; automotive electronics; electric control equipment; fault diagnosis; inference mechanisms; neural nets; pattern classification; sensor fusion; D-S evidence information fusing method; actuator fault; automobile electric controlled system; data flow analysis; fault diagnosis; fault recognition; neural network; on-board diagnosis; pattern classification; pattern recognition; sensor fault; Acceleration; Actuators; Automobiles; Control systems; Fault diagnosis; Fires; Ignition; Pattern recognition; Petroleum; Temperature sensors; Classification; Data flow; Electric controlled system; Fusing; Neural Network; Pattern recognition;
Conference_Titel :
Information and Automation (ICIA), 2010 IEEE International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-5701-4
DOI :
10.1109/ICINFA.2010.5512487
