Predictive maintenance is one of the technology enablers in the railway industry to get rid of fixed service intervals and switch to maintenance on demand to reduce life cycle costs. Until now inspection intervals on regular basis lead to high costs to ensure the overall goal of high availability. To strengthen the condition-triggered maintenance this work presents a cubature Kalman filter approach for parameter identification of complex railway suspension systems. In detail, the approach is designed for the identification of the spring stiffness and damping coefficient of the secondary suspension system using measurements from real world operation. The parametrization of the filter is performed in a way that the filter shows a so called consistency property which ensures a statistical correct behaviour. Furthermore the cubature Kalman filter approach shows promising properties regards computational complexity in combination with the achievable accuracy.


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    Title :

    Parameter Identification for Railway Suspension Systems Using Cubature Kalman Filter


    Additional title:

    Lect.Notes Mechanical Engineering



    Conference:

    The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2019 ; Gothenburg, Sweden August 12, 2019 - August 16, 2019



    Publication date :

    2020-02-13


    Size :

    5 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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