A switch model approach for uego sensor diagnostics in the presence of asymmetric faults

Hassene Jammoussi, Matthew A. Franchek, Karolos Grigoriadis, Imad H. Makki, Stephen B. Smith, James M. Kerns

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

4 Scopus citations

Abstract

In this paper a diagnostics approach for the universal exhaust gas oxygen (UEGO) sensor in spark ignition (SI) internal combustion engines is proposed. Due to possible additional lag or delay in the transition of the air-fuel ratio (AFR) from lean to rich or rich to lean, the sensor measurement affects the closed-loop performance and may lead to instability of the feedback system. The paper discusses each of these faults and proposes a universal switch model approach that splits the operation of the sensor into lean and rich separate models to monitor both responses and enable the diagnostics of the UEGO sensor. The approach transforms the SISO system to a two-input single-output system and the estimated parameters of the system will identify the lag and delay in both burn modes to detect, isolate and estimate the symmetric/asymmetric faults. The method is demonstrated on a simulated air fuel (AF) closed loop system regulated by a PI controller together with a Smith predictor for the delay compensation. The method is then applied on experimental data obtained by inducing faults on the sensor.

Original languageEnglish (US)
Title of host publicationProceedings of the 13th IASTED International Conference on Control and Applications, CA 2011
Pages160-164
Number of pages5
DOIs
StatePublished - 2011
Event13th IASTED International Conference on Control and Applications, CA 2011 - Vancouver, BC, Canada
Duration: Jun 1 2011Jun 3 2011

Publication series

NameProceedings of the 13th IASTED International Conference on Control and Applications, CA 2011

Other

Other13th IASTED International Conference on Control and Applications, CA 2011
CountryCanada
CityVancouver, BC
Period6/1/116/3/11

Keywords

  • Air-fuel ratio dynamics
  • SI engines
  • System identification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering

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