Rail Wear Estimation for Predictive Maintenance: a strategic approach

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    Abstract

    Since the very beginning of rail transport, wear has been identified as one of the dominant damage mechanisms that influence the Remaining Useful Life (RUL) of rail tracks. Whereas maintenance of the track is now predominantly executed at fixed intervals or based on yearly inspections, the accurate prediction of rail wear could considerably improve the maintenance process. The present work proposes a method for long-term rail wear prediction using measurements of actual rail and wheel profiles as starting point. By doing so, the computational expensive step of updating the rail profile in a wear calculation, as is done in presently used methods, can be omitted. The proposed method is used to study a number of generic trends, varying curve radius and rail or wheel profile. Further, the method is validated against measured wear on actual track sections for moderate curves. Finally, it can easily be extended to include variations in operational usage of the track (type / weight of trains, geometric details, slip conditions) in the future. The method presented in this paper can therefore assist in improving the track maintenance process by maximizing the utilization of the track service life, and minimizing maintenance costs.
    Original languageEnglish
    Title of host publicationProceedings of the European Conference of the PHM Society
    EditorsChetan Kulkarni, Tiedo Tinga
    Place of PublicationUtrecht
    PublisherPHM society
    Number of pages11
    Volume4
    Edition1
    Publication statusPublished - 6 Jul 2018
    Event4th European Conference of the Prognostics and Health Management Society, PHME 2018 - Muntgebouw Utrecht, Utrecht, Netherlands
    Duration: 3 Jul 20186 Jul 2018
    Conference number: 4
    http://www.phmsociety.org/events/conference/phm/europe/18

    Conference

    Conference4th European Conference of the Prognostics and Health Management Society, PHME 2018
    Abbreviated titlePHME 2018
    CountryNetherlands
    CityUtrecht
    Period3/07/186/07/18
    Internet address

    Keywords

    • Wear
    • multi-body dynamics

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  • Cite this

    Meghoe, A. A., Loendersloot, R., Bosman, R., & Tinga, T. (2018). Rail Wear Estimation for Predictive Maintenance: a strategic approach. In C. Kulkarni, & T. Tinga (Eds.), Proceedings of the European Conference of the PHM Society (1 ed., Vol. 4). [370] Utrecht: PHM society.