Rail wear and remaining life prediction using meta-models

Annemieke Meghoe*, Richard Loendersloot, Tiedo Tinga

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

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    Abstract

    The study presented in this paper proposes a method to estimate the Remaining Useful Life (RUL) of railway tracks determined by wear and taking into account various track geometry and usage profile parameters. The relation between these parameters and rail wear is established by means of meta-models derived from physical models. These models are obtained with regression analysis where the best fit is found from a relatively large set of numerical experiments for various scenarios. The specific parameter settings for these scenarios are obtained by using the Latin Hypercube Sampling (LHS) method. Furthermore, for the rail profile, which is one of the input parameters for the meta-model, it is shown that the evolution due to wear in moderate curves can be characterized by only one parameter. The findings in this work including are valuable for Infrastructure Managers (IMs) and can easily be implemented in maintenance decision support tools.
    Original languageEnglish
    Pages (from-to)1-26
    Number of pages26
    JournalInternational Journal of Rail Transportation
    Volume8
    Issue number1
    Early online date2 Jun 2019
    DOIs
    Publication statusPublished - 2 Jan 2020

    Fingerprint

    Rails
    Wear of materials
    scenario
    Regression analysis
    Managers
    Sampling
    German Federal Railways
    regression analysis
    Geometry
    mathematics
    manager
    infrastructure
    experiment
    Experiments

    Keywords

    • Wear
    • Wheel-rail contact
    • Multi-body dynamic simulation
    • Meta-modeling
    • Latin hypercube sampling

    Cite this

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    title = "Rail wear and remaining life prediction using meta-models",
    abstract = "The study presented in this paper proposes a method to estimate the Remaining Useful Life (RUL) of railway tracks determined by wear and taking into account various track geometry and usage profile parameters. The relation between these parameters and rail wear is established by means of meta-models derived from physical models. These models are obtained with regression analysis where the best fit is found from a relatively large set of numerical experiments for various scenarios. The specific parameter settings for these scenarios are obtained by using the Latin Hypercube Sampling (LHS) method. Furthermore, for the rail profile, which is one of the input parameters for the meta-model, it is shown that the evolution due to wear in moderate curves can be characterized by only one parameter. The findings in this work including are valuable for Infrastructure Managers (IMs) and can easily be implemented in maintenance decision support tools.",
    keywords = "Wear, Wheel-rail contact, Multi-body dynamic simulation, Meta-modeling, Latin hypercube sampling",
    author = "Annemieke Meghoe and Richard Loendersloot and Tiedo Tinga",
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    doi = "10.1080/23248378.2019.1621780",
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    Rail wear and remaining life prediction using meta-models. / Meghoe, Annemieke; Loendersloot, Richard ; Tinga, Tiedo .

    In: International Journal of Rail Transportation, Vol. 8, No. 1, 02.01.2020, p. 1-26.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Rail wear and remaining life prediction using meta-models

    AU - Meghoe, Annemieke

    AU - Loendersloot, Richard

    AU - Tinga, Tiedo

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    AB - The study presented in this paper proposes a method to estimate the Remaining Useful Life (RUL) of railway tracks determined by wear and taking into account various track geometry and usage profile parameters. The relation between these parameters and rail wear is established by means of meta-models derived from physical models. These models are obtained with regression analysis where the best fit is found from a relatively large set of numerical experiments for various scenarios. The specific parameter settings for these scenarios are obtained by using the Latin Hypercube Sampling (LHS) method. Furthermore, for the rail profile, which is one of the input parameters for the meta-model, it is shown that the evolution due to wear in moderate curves can be characterized by only one parameter. The findings in this work including are valuable for Infrastructure Managers (IMs) and can easily be implemented in maintenance decision support tools.

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    KW - Wheel-rail contact

    KW - Multi-body dynamic simulation

    KW - Meta-modeling

    KW - Latin hypercube sampling

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