Analysis of autoregressive coefficients of knock sensor signals for misfire detection in internal combustion engines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current emission laws require a misfire detection rate of 100% which can only be robustly achieved by the combination of different detection strategies. State of the art detection is done by analyzing the engine speed drops. In order to maximize the detection rate, vibration signals acquired by a broadband knock sensor are investigated for their suitability for misfire detection. In this work the vibration signal is modeled as an autoregressive process described by polynomial filter coefficients. The influences of misfire events on those filter coefficients are analyzed. As a second method, the power of the vibration signal is analyzed. Cross-influences on the autoregressive coefficients from the engine speed are analyzed. Those influences have to be considered when designing an adaptive threshold-based method for misfire detection based on the presented methods.

Original languageEnglish
Title of host publicationI2MTC 2019 - 2019 IEEE International Instrumentation and Measurement Technology Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781538634608
DOIs
Publication statusPublished - 1 May 2019
Event2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019 - Auckland, New Zealand
Duration: 20 May 201923 May 2019

Publication series

NameConference Record - IEEE Instrumentation and Measurement Technology Conference
Volume2019-May
ISSN (Print)1091-5281

Conference

Conference2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019
CountryNew Zealand
CityAuckland
Period20/05/1923/05/19

Keywords

  • AR modeling
  • Knock sensor
  • Misfire detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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