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Model-based Fault Detection for an Electro-hydraulic System Using Extended Kalman Filter

Zheng, Lin, Shi, John Z. and Ball, Andrew (2007) Model-based Fault Detection for an Electro-hydraulic System Using Extended Kalman Filter. In: Second World Congress of Asset Management and the Fourth International Conference on Condition Monitoring, 11th - 14th June 2007, Harrogate, UK.

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    Abstract

    This paper applies extended Kalman filter (EKF) for model-based fault detection of an electro-hydraulic system to deal with stochastic behaviour during control. Kalman filter (KF) is a powerful means of estimation, even when the precise nature of the modelled system is unknown. After a brief introduction of the KF and EKF, an EKF is chosen for this application. A mathematical model of an electro-hydraulic system is then developed. Some faults are introduced to evaluate the EKF fault detection method. Comparison of the EKF estimation accuracy and a linearised model-based accuracy shows the advantage of the EKF. The fault detection result shows that the EKF provides a good estimation of the system with stochastic performance for model-based fault detection and diagnosis.

    Item Type: Conference or Workshop Item (Paper)
    Subjects: T Technology > TJ Mechanical engineering and machinery
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    School of Computing and Engineering > Automotive Engineering Research Group
    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
    Related URLs:
    Depositing User: Zhanqun Shi
    Date Deposited: 22 Jul 2009 14:31
    Last Modified: 03 Dec 2010 12:12
    URI: http://eprints.hud.ac.uk/id/eprint/4360

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