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TECHNICAL PAPERS

Intake Air Path Diagnostics for Internal Combustion Engines

[+] Author and Article Information
Matthew A. Franchek1

 University of Houston, Houston, TX 77204-4006mfranchek@uh.edu

Patrick J. Buehler

School of Mechanical Engineering, Purdue University, 1077 Ray W. Herrick Laboratories, West Lafayette, IN 47907

Imad Makki

Advanced Powertrain Control Systems, Ford Motor Company, FPC-B, MD37, 760 Towncenter Dr., Dearborn, MI 48126

1

Corresponding author.

J. Dyn. Sys., Meas., Control 129(1), 32-40 (May 29, 2006) (9 pages) doi:10.1115/1.2397150 History: Received May 22, 2005; Revised May 29, 2006

Presented is the detection, isolation, and estimation of faults that occur in the intake air path of internal combustion engines during steady state operation. The proposed diagnostic approach is based on a static air path model, which is adapted online such that the model output matches the measured output during steady state conditions. The resulting changes in the model coefficients create a vector whose magnitude and direction are used for fault detection and isolation. Fault estimation is realized by analyzing the residual between the actual sensor measurement and the output of the original (i.e., healthy) model. To identify the structure of the steady state air path model a process called system probing is developed. The proposed diagnostics algorithm is experimentally validated on the intake air path of a Ford 4.6L V-8 engine. The specific faults to be identified include two of the most problematic faults that degrade the performance of transient fueling controllers: bias in the mass air flow sensor and a leak in the intake manifold. The selected model inputs include throttle position and engine speed, and the output is the mass air flow sensor measurement.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Power spectral density of MAF sensor output with engine speed fixed and a 10rad∕s throttle excitation

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Figure 2

Power spectral density of MAF sensor output with throttle fixed and a 6rad∕s engine speed excitation

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Figure 3

Power spectral density of the MAF sensor output with 6 and 10rad∕s sine wave excitation from engine speed and throttle

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Figure 4

Comparison of MAF sensor output and predicted output before and after adaptation with a MAF sensor bias

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Figure 5

Comparison of MAF sensor output and predicted output before and after adaptation with an intake manifold leak

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Figure 6

Estimation of the fault size due to MAF sensor bias

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Figure 7

Estimation of the fault size due to a manifold leak

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Figure 8

Estimation of the fault size due to a manifold leak, expanded plot

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