In recent years, significant research transpired on onboard monitoring of various phenomena arising in dynamic vehicle-track interaction. One key issue being monitoring of vehicle hunting instability. Current hunting detection standards are appropriate for certification tests of vehicles, but incapable to monitor the health of the vehicle and track subsystems influencing the hunting instability. This paper proposes a signal based procedure for accurately triggering Hunting/No-Hunting alarm by conforming to requirements of onboard monitoring. A new method is conceived to reveal coherence among lateral and longitudinal accelerations during vehicle hunting. Furthermore, an index which amalgamates phase and amplitude information of lateral and longitudinal axlebox accelerations is introduced to detect coupled modes in lateral and yaw directions, i.e. hunting modes. Several simulations based pragmatic case studies are performed to assess the efficacy of the proposed procedure. The proposed method outperforms traditional hunting detection procedures by detecting more Hunting/No-Hunting occurrences. The proposed method contributes towards digitalization of rail vehicles through condition-based and predictive maintenance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle running instability detection algorithm (VRIDA): A signal based onboard diagnostic method for detecting hunting instability of rail vehicles


    Contributors:
    Kulkarni, R (author) / Qazizadeh, A (author) / Berg, M (author) / Carlsson, U (author) / Stichel, S (author)


    Publication date :

    2022-03-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)

    Kulkarni, Rohan / Qazizadeh, Alireza / Berg, Mats | BASE | 2023

    Free access

    iVRIDA-fleet: unsupervised rail vehicle running instability detection algorithm for passenger vehicle fleet

    Kulkarni, Rohan / Berg, Mats / Qazizadeh, Alireza et al. | Taylor & Francis Verlag | 2025

    Free access

    A signal analysis based hunting instability detection methodology for high-speed railway vehicles

    Sun, Jianfeng / Meli, Enrico / Cai, Wubin et al. | Taylor & Francis Verlag | 2021


    iVRIDA: intelligent Vehicle Running Instability Detection Algorithm for high-speed rail vehicles using Temporal Convolution Network - A pilot study

    Kulkarni, Rohan R. / Giossi, Rocco Libero / Damsongsaeng, Prapanpong et al. | TIBKAT | 2022

    Free access

    Limit wheel profile for hunting instability of railway vehicles

    Mazzola, Laura / Alfi, Stefano / Braghin, F. et al. | Tema Archive | 2010