Application of neural networks to detecting misfire in automotive engines

William B. Ribbens, Jaehong Park, Daeeun Kim

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

This paper presents a novel application of neural networks to a vexing practical problem in the automotive industry. By government regulations, automobiles are required to be equipped with instrumentation to detect engine misfires and to alert the driver whenever the misfire rate has the potential to affect the health of emission control systems. A relevant model for the powertrain dynamics is developed in this paper as well as an explanation of the instrumentation. The basis for using a neural network to detect these misfires is explained and experimental system performance data (including error rates) are given. It is shown in this paper that the present method has the potential to meet the government mandated requirements.

Original languageEnglish
Article number389586
Pages (from-to)II593-II596
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
DOIs
Publication statusPublished - 1994
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: 1994 Apr 191994 Apr 22

Bibliographical note

Publisher Copyright:
© 1994 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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