Frequency selection with oscillatory neurons for engine misfire detection

Author

Kim, DaeEun ; Park, Jaehong

Author_Institution

Dept. of Artificial Intelligence, Edinburgh Univ., UK

Volume

4

fYear

1999

fDate

1999

Firstpage

2649

Abstract

Engine misfire detection is one of the great issues in automobile systems to inform incomplete engine exhaustion which causes environmental problem and also to guarantee safe operation of the vehicles. It requires continuous monitoring of the system in real-time to detect deviations from the normal signal patterns. This paper presents a special frequency selection method based on recurrent neural networks consisting of oscillatory neurons, and applies the method with the genetic algorithm to engine misfire detection problem by observing the engine speed in an automobile system

Keywords

automobiles; computerised monitoring; fault diagnosis; genetic algorithms; internal combustion engines; pattern recognition; real-time systems; recurrent neural nets; automobile; engine exhaustion; engine misfire detection; frequency selection; genetic algorithm; monitoring; oscillatory neurons; real-time systems; recurrent neural networks; signal pattern recognition; Automobiles; Engines; Environmental factors; Frequency; Monitoring; Neurons; Real time systems; Recurrent neural networks; Vehicle detection; Vehicle safety;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.833495

Filename

833495