Title :
Frequency selection with oscillatory neurons for engine misfire detection
Author :
Kim, DaeEun ; Park, Jaehong
Author_Institution :
Dept. of Artificial Intelligence, Edinburgh Univ., UK
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.833495
