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Predictive maintenance in railway systems: MBS-based wheel and rail life prediction exemplified for the Swedish Iron-Ore line
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.ORCID iD: 0000-0002-6346-6620
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.ORCID iD: 0000-0003-1583-4625
Swedish Transport Administration, Luleå, Sweden.
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2022 (English)In: Vehicle System Dynamics, ISSN 0042-3114, E-ISSN 1744-5159, p. 1-18Article in journal (Refereed) Published
Abstract [en]

A successful predictive maintenance strategy for wheels and rails depends on an accurate and robust modelling of damage evolution, mainly uniform wear and Rolling Contact Fatigue (RCF). In this work a life prediction framework for wheels and rails is presented. The prediction model accounts for wear, RCF, and their interaction based on the output from MBS simulations to calculate the remaining life of the asset, given in mileage for wheels and MGTs for rails. Once the model is calibrated, the proposed methodology can predict the sensitivity of the maintenance intervals against changes in operational conditions, such as changes in contact lubrication, track gauge, operating speeds, etc. The prediction framework is then used in two operational cases on the Swedish Iron-Ore line. The studied cases are, the analysis of wheel life for the locomotives, and the analysis of rail life for gauge widening scenarios. The results demonstrate the capabilities of the MBS-based damage modelling for predictive maintenance purposes and showcase how these techniques can set the path towards Digital Twins of railway assets.

Place, publisher, year, edition, pages
Informa UK Limited , 2022. p. 1-18
Keywords [en]
Digital twin, dynamic simulations, predictive maintenance, RCF, wear
National Category
Other Civil Engineering Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-335756DOI: 10.1080/00423114.2022.2161920ISI: 000906257000001Scopus ID: 2-s2.0-85145471795OAI: oai:DiVA.org:kth-335756DiVA, id: diva2:1795804
Note

QC 20230911

Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-11Bibliographically approved

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Hossein Nia, SaeedCasanueva, CarlosStichel, Sebastian

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Hossein Nia, SaeedFlodin, JesperCasanueva, CarlosStichel, Sebastian
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