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Research Papers

Real-Time Diagnosis of the Exhaust Recirculation in Diesel Engines Using Least-Squares Parameter Estimation

[+] Author and Article Information
Javad Mohammadpour1

Department of Mechanical Engineering, University of Houston, Houston, TX 77204jmohammadpour@uh.edu

Karolos Grigoriadis, Matthew Franchek

Department of Mechanical Engineering, University of Houston, Houston, TX 77204

Benjamin J. Zwissler

Common Diagnostics/OBD, Cummins Inc., Columbus, IN 47201

1

Corresponding author.

J. Dyn. Sys., Meas., Control 132(1), 011009 (Dec 17, 2009) (8 pages) doi:10.1115/1.4000655 History: Received June 12, 2008; Revised March 12, 2009; Published December 17, 2009; Online December 17, 2009

In this paper, we present a real-time parameter identification approach for diagnosing faults in the exhaust gas recirculation (EGR) system of Diesel engines. The proposed diagnostics method has the ability to detect and estimate the magnitude of a leak or a restriction in the EGR valve, which are common faults in the air handling system of a Diesel engine. Real-time diagnostics is achieved using a recursive-least-squares (RLS) method, as well as, a recursive formulation of a more robust version of the RLS method referred to as recursive total-least-squares method. The method is used to identify the coefficients in a static orifice flow model of the EGR valve. The proposed approach of fault detection is successfully applied to diagnose low-flow or high-flow faults in an engine and is validated using experimental data obtained from a Diesel engine test cell and a truck.

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Figures

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Figure 9

Convergence of the identified coefficients a1 and a2 for the healthy case (top), 43 mm leak (middle), and 45 mm leak (bottom) using RLS and RTLS versus the sample number with the sampling rate of 6 Hz

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Figure 1

Simplified schematic view of the airpath in a Diesel engine

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Figure 2

Results of the parameter identification using the RLS and RTLS algorithms: (a) baseline and (b) restricted flow condition

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Figure 3

Coefficient a1 identified using the RTLS algorithm versus the percentage unrestricted EGR valve area

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Figure 4

Coefficient a2 identified using the RTLS algorithm versus the percentage unrestricted EGR valve area

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Figure 5

Identification of the coefficients a1 and a2 using the RTLS algorithm for the baseline system with a sampling rate of 6 Hz

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Figure 6

Identification of coefficients a1 and a2 for a 95% EGR valve restriction using the RTLS algorithm with a sampling rate of 6 Hz

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Figure 7

Histograms of the identified baseline coefficients a1 and a2

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Figure 8

The EGR mass flow residual signal calculated using the measurement and output of the adapted healthy model

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