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ADAPTIVE FDI FOR AUTOMOTIVE ENGINE AIR PATH AND ROBUSTNESS ASSESSMENT UNDER CLOSED-LOOP CONTROL  

Sangha, M.S. (Control Systems Research Group, School of Engineering, Liverpool John Moores University)
Yu, D.L. (Control Systems Research Group, School of Engineering, Liverpool John Moores University)
Gomm, J.B. (Control Systems Research Group, School of Engineering, Liverpool John Moores University)
Publication Information
International Journal of Automotive Technology / v.8, no.5, 2007 , pp. 637-650 More about this Journal
Abstract
A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.
Keywords
On-board fault diagnosis; Automotive engines; Adaptive neural networks; Adaptive fault classification;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 0
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