Application of neural networks to detecting misfire in automotive engines

William B. Ribbens; Jaehong Park; Daeeun Kim

doi:10.1109/ICASSP.1994.389586

Application of neural networks to detecting misfire in automotive engines

William B. Ribbens, Jaehong Park, Daeeun Kim

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Article number	389586
Pages (from-to)	II593-II596
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2
DOIs	https://doi.org/10.1109/ICASSP.1994.389586
Publication status	Published - 1994
Event	Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust Duration: 1994 Apr 19 → 1994 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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10.1109/ICASSP.1994.389586

Cite this

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title = "Application of neural networks to detecting misfire in automotive engines",

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.",

author = "Ribbens, {William B.} and Jaehong Park and Daeeun Kim",

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doi = "10.1109/ICASSP.1994.389586",

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AU - Ribbens, William B.

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AU - Kim, Daeeun

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N2 - 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.

AB - 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.

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T2 - Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6)

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ER -

Application of neural networks to detecting misfire in automotive engines

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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