A fuzzy system for automotive fault diagnosis: fast rule generation and self-tuning

Author

Lu, Yi ; Chen, Tie Qi ; Hamilton, Brennan

Author_Institution

Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA

Volume

49

Issue

2

fYear

2000

fDate

3/1/2000 12:00:00 AM

Firstpage

651

Lastpage

660

Abstract

This paper describes a fuzzy model that learns automotive diagnostic knowledge through machine learning techniques. The fuzzy model contains the algorithms for automatically generating fuzzy rules and optimizing fuzzy membership functions. The fuzzy model has been implemented to detect a vacuum leak in the electronic engine controller (EEC) as part of the end-of-line test at automotive assembly plants. The implemented system has been tested extensively, and its performance is presented

Keywords

automotive electronics; fault diagnosis; fuzzy set theory; internal combustion engines; leak detection; automotive assembly plants; automotive diagnostic knowledge; automotive fault diagnosis; electronic engine controller; end-of-line test; fast rule generation; fuzzy membership functions optimisation; fuzzy model; fuzzy rules generation; fuzzy system; machine learning techniques; self-tuning; vacuum leak detection; Automatic control; Automotive engineering; Electronic equipment testing; Engines; Fault diagnosis; Fuzzy control; Fuzzy systems; Leak detection; Machine learning; Machine learning algorithms;

fLanguage

English

Journal_Title

Vehicular Technology, IEEE Transactions on

Publisher

ieee

ISSN

0018-9545

Type

jour

DOI

10.1109/25.832997

Filename

832997