Using data analytics to understand why certain rail sections at the Dutch high-speed line are affected by RCF

Conference paper (2017)

Authors

Ricks Schalk Student, Mott MacDonald
A. Zoeteman Integral Design & Management - CEG
Alfredo Nunez Railway Engineering - CEG
A.R.M. Wolfert Integral Design & Management - CEG , Materials and Environment - CEG
Research Group
Integral Design & Management (CEG) (TU Delft)
Copyright: © 2017 Ricks Schalk, A. Zoeteman, Alfredo Nunez, A.R.M. Wolfert
DOI
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https://doi.org/10.25084/raileng.2017.0096
Data Analytics Rolling Stock High-Speed Rail Rolling Contact Fatigue (RCF) Rail Maintenance
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Published Date

2017

Language

English

Reuse Rights

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Faculty

Civil Engineering & Geosciences

Department

Materials- Mechanics- Management & Design

Research Group

Integral Design & Management

Abstract

This paper describes the use of big data for analysing Rolling Contact Fatigue (RCF) phenomena at the High Speed Line (HSL Zuid) in The Netherlands. The authors developed a data model to investigate the impacting parameters in train-track interaction. This has been done to gain more insight in the circumstances where RCF occurs and to conclude why some track sections are severely affected and others not.
To evaluate the worst affected areas by RCF, the methodology included a bottom-up approach which focuses at the worst affected areas by RCF, developing a set of characteristic parameter values regarding different types of hotspots. The methodology has been applied for the Dutch High-speed line, where certain sections had been heavily affected by RCF. Findings concluded that slow running traffic through curves on a high-speed line is likely to contribute to the appearance of RCF.