A robust data-driven method for on-board vibration-based degradation detection in railway suspensions of running vehicles is introduced. The method employs two lateral vibration acceleration sensors per vehicle half, one on the bogie and one on the vehicle body. It is based on Transmittance Function data-driven models of the AutoRegressive with eXogenous excitation type within an unsupervised Multiple Model framework and aims at effective detection of early-stage component degradation while achieving robustness to varying Operating Conditions. The method is validated via thousands of Monte Carlo simulation experiments under three distinct travelling speeds. Through them, perfect detection performance is demonstrated for ‘small’ level degradation, characterised by $ 20\% $ 20 % reduction in the properties of suspension components, while even ‘minor’ degradation, characterised by $ 10\% $ 10 % reduction, is detectable but somewhat less effectively. The very good performance characteristics of the method are confirmed via field tests as well, while its superiority over alternative schemes is demonstrated via comparisons with a state-of-the-art entropy-based approach.


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    Titel :

    On-board vibration-based robust and unsupervised degradation detection in railway suspensions under various travelling speeds via a Multiple Model framework


    Beteiligte:

    Erschienen in:

    Vehicle System Dynamics ; 62 , 6 ; 1446-1470


    Erscheinungsdatum :

    2024-06-02


    Format / Umfang :

    25 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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