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

Ricks Schalk, Arjen Zoeteman, Alfredo Nunez, Rogier Wolfert

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of 14th International Railway Engineering-2017conference in Edinburgh, UK: 2017
Number of pages11
DOIs
Publication statusPublished - 2017
Event14th International Conference and Exhibition Railway Engineering 2017 - Edinburgh, United Kingdom
Duration: 20 Jun 201722 Jun 2017
Conference number: 14
http://www.railwayengineering.com/

Conference

Conference14th International Conference and Exhibition Railway Engineering 2017
Abbreviated titleRailway Engineering 2017
CountryUnited Kingdom
CityEdinburgh
Period20/06/1722/06/17
Internet address

Keywords

  • High-Speed Rail
  • Rail Maintenance
  • Rolling Stock
  • Data Analytics
  • Rolling Contact Fatigue (RCF)

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