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Model-based Fault Detection and Diagnosis of the Anti-lock Braking System

Elshanti, Ali, Shi, John Z., Badri, A, Gu, Fengshou and Ball, Andrew (2007) Model-based Fault Detection and Diagnosis of the Anti-lock Braking System. In: Second World Congress of Asset Management and the Fourth International Conference on Condition Monitoring (WCEAM), June 2007, Harrogate, UK.

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    Abstract

    The ABS is one of the latest improvements to the braking system, which prevent the vehicle’s brakes from locking up and skidding during hard stops on icy or wet roads. ABS controllers are characterized by robust adaptive behaviour with respect to highly uncertain tyre characteristics and fast changing road surface properties. Performance improvement is typically sought in the areas of stability, steerability and stopping distance. In this paper, a non-linear mathematical model of the ABS is developed and ABS system is modelled using Simulink and some of the results are displayed which demonstrate the potential of the proposed model-based prognostics approach.

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: Published on CD ROM, ISBN 9781901892222
    Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Automotive Engineering Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
    School of Computing and Engineering > Informatics Research Group
    School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
    Related URLs:
    Depositing User: Zhanqun Shi
    Date Deposited: 19 Aug 2009 10:29
    Last Modified: 08 Dec 2010 13:24
    URI: http://eprints.hud.ac.uk/id/eprint/4363

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