Statistics based detection and isolation of UEGO sensor faults

Hassene Jammoussi; Imad Makki; Dimitar Filev; Matthew Franchek

doi:10.1115/DSCC2013-3798

Statistics based detection and isolation of UEGO sensor faults

Hassene Jammoussi, Imad Makki, Dimitar Filev, Matthew Franchek

Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Stringent emission regulations mandated by California air regulation board (CARB) require monitoring the upstream exhaust gas oxygen (UEGO) sensor for any possible malfunction causing the vehicle emissions to exceed certain thresholds. Six faults have been identified to potentially cause the UEGO sensor performance to deteriorate and potentially lead to instability of the air-fuel ratio (AFR) control loop. These malfunctions are either due to an additional delay or an additional lag in the transition of the sensor response from lean to rich or rich to lean. Current technology detects the faults the same way (approximated by a delay type fault) and does not distinguish between the different faults. In the current paper, a statistics based approach is developed to diagnose these faults. Specifically, the characteristics of a non-normal distribution function are estimated based on the UEGO sensor output and used to detect and isolate the faults. When symmetric operation is detected, a system identification process is employed to estimate the parameters of the dynamic system and determine the type of operation. The proposed algorithm has been demonstrated on real data obtained from both Ford F150 and Mustang V6 vehicles.

Original language	English (US)
Title of host publication	Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing;
Publisher	American Society of Mechanical Engineers (ASME)
Volume	3
ISBN (Print)	9780791856147
DOIs	https://doi.org/10.1115/DSCC2013-3798
State	Published - Jan 1 2013
Event	ASME 2013 Dynamic Systems and Control Conference, DSCC 2013 - Palo Alto, CA, United States Duration: Oct 21 2013 → Oct 23 2013

Other

Other	ASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Country/Territory	United States
City	Palo Alto, CA
Period	10/21/13 → 10/23/13

ASJC Scopus subject areas

Control and Systems Engineering

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10.1115/DSCC2013-3798

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Jammoussi, H., Makki, I., Filev, D., & Franchek, M. (2013). Statistics based detection and isolation of UEGO sensor faults. In Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; (Vol. 3). [V003T42A001] American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2013-3798

Statistics based detection and isolation of UEGO sensor faults. / Jammoussi, Hassene; Makki, Imad; Filev, Dimitar et al.

Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing;. Vol. 3 American Society of Mechanical Engineers (ASME), 2013. V003T42A001.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jammoussi, H, Makki, I, Filev, D & Franchek, M 2013, Statistics based detection and isolation of UEGO sensor faults. in Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing;. vol. 3, V003T42A001, American Society of Mechanical Engineers (ASME), ASME 2013 Dynamic Systems and Control Conference, DSCC 2013, Palo Alto, CA, United States, 10/21/13. https://doi.org/10.1115/DSCC2013-3798

Jammoussi H, Makki I, Filev D, Franchek M. Statistics based detection and isolation of UEGO sensor faults. In Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing;. Vol. 3. American Society of Mechanical Engineers (ASME). 2013. V003T42A001 https://doi.org/10.1115/DSCC2013-3798

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abstract = "Stringent emission regulations mandated by California air regulation board (CARB) require monitoring the upstream exhaust gas oxygen (UEGO) sensor for any possible malfunction causing the vehicle emissions to exceed certain thresholds. Six faults have been identified to potentially cause the UEGO sensor performance to deteriorate and potentially lead to instability of the air-fuel ratio (AFR) control loop. These malfunctions are either due to an additional delay or an additional lag in the transition of the sensor response from lean to rich or rich to lean. Current technology detects the faults the same way (approximated by a delay type fault) and does not distinguish between the different faults. In the current paper, a statistics based approach is developed to diagnose these faults. Specifically, the characteristics of a non-normal distribution function are estimated based on the UEGO sensor output and used to detect and isolate the faults. When symmetric operation is detected, a system identification process is employed to estimate the parameters of the dynamic system and determine the type of operation. The proposed algorithm has been demonstrated on real data obtained from both Ford F150 and Mustang V6 vehicles.",

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N2 - Stringent emission regulations mandated by California air regulation board (CARB) require monitoring the upstream exhaust gas oxygen (UEGO) sensor for any possible malfunction causing the vehicle emissions to exceed certain thresholds. Six faults have been identified to potentially cause the UEGO sensor performance to deteriorate and potentially lead to instability of the air-fuel ratio (AFR) control loop. These malfunctions are either due to an additional delay or an additional lag in the transition of the sensor response from lean to rich or rich to lean. Current technology detects the faults the same way (approximated by a delay type fault) and does not distinguish between the different faults. In the current paper, a statistics based approach is developed to diagnose these faults. Specifically, the characteristics of a non-normal distribution function are estimated based on the UEGO sensor output and used to detect and isolate the faults. When symmetric operation is detected, a system identification process is employed to estimate the parameters of the dynamic system and determine the type of operation. The proposed algorithm has been demonstrated on real data obtained from both Ford F150 and Mustang V6 vehicles.

AB - Stringent emission regulations mandated by California air regulation board (CARB) require monitoring the upstream exhaust gas oxygen (UEGO) sensor for any possible malfunction causing the vehicle emissions to exceed certain thresholds. Six faults have been identified to potentially cause the UEGO sensor performance to deteriorate and potentially lead to instability of the air-fuel ratio (AFR) control loop. These malfunctions are either due to an additional delay or an additional lag in the transition of the sensor response from lean to rich or rich to lean. Current technology detects the faults the same way (approximated by a delay type fault) and does not distinguish between the different faults. In the current paper, a statistics based approach is developed to diagnose these faults. Specifically, the characteristics of a non-normal distribution function are estimated based on the UEGO sensor output and used to detect and isolate the faults. When symmetric operation is detected, a system identification process is employed to estimate the parameters of the dynamic system and determine the type of operation. The proposed algorithm has been demonstrated on real data obtained from both Ford F150 and Mustang V6 vehicles.

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Statistics based detection and isolation of UEGO sensor faults

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