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Prediction Models for Track Deterioration
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0003-3911-8009
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi. Swedish Transportation Administration, Sweden.
2019 (engelsk)Inngår i: Proceedings of the International Heavy Haul Conference,, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The Swedish Iron ore line is subjected to sub-arctic weather conditions, high axle loads and a large yearly tonnage. These operational conditions challenge the usefulness of existing deterministic deterioration models for railway infrastructure that are validated in other contexts. However, more frequent and higher precision of condition measurements, combined with statistically based prediction methods, may offer a viable solution. Here, we study remaining useful life predictions of railway track geometry properties based on recursively updated time series. We discuss how data-driven models are affected by measurement errors of track properties, such models’ ability to detect seasonal effects, and how they are affected by irregular sampling. The prediction abilities and uncertainty measures for different modelling approaches are also compared.

sted, utgiver, år, opplag, sider
2019.
HSV kategori
Forskningsprogram
Kvalitetsteknik och logistik
Identifikatorer
URN: urn:nbn:se:ltu:diva-83468OAI: oai:DiVA.org:ltu-83468DiVA, id: diva2:1541354
Konferanse
The International Heavy Haul Conference
Tilgjengelig fra: 2021-03-31 Laget: 2021-03-31 Sist oppdatert: 2023-09-16

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