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

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

Schalk, Ricks (Mott MacDonald; Student TU Delft)
Zoeteman, A. (TU Delft Integral Design & Management)
Nunez, Alfredo (TU Delft Railway Engineering)
Wolfert, A.R.M. (TU Delft Integral Design & Management; TU Delft Materials and Environment)

2017

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.

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

http://resolver.tudelft.nl/uuid:39ec647a-8a00-4f0d-834b-2cfcb43178f5

10.25084/raileng.2017.0096

Proceedings of 14th International Railway Engineering-2017conference in Edinburgh, UK: 2017

14th International Conference and Exhibition Railway Engineering 2017, 2017-06-20 → 2017-06-22, Edinburgh, United Kingdom

Institutional Repository

conference paper

© 2017 Ricks Schalk, A. Zoeteman, Alfredo Nunez, A.R.M. Wolfert