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Augmented asset management in railways - Issues and challenges in rolling stock
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-2153-2914
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-0055-2740
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-1938-0985
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-9992-7791
2022 (English)In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 236, no 7, p. 850-862Article in journal (Refereed) Published
Abstract [en]

Managing assets in railway, including infrastructure and rolling stock, efficiently and effectively is challenging. The emerging digital technologies and Artificial Intelligence (AI) are expected to augment the decision making in Asset Management (AM) and Fleet Management (FM). The AI technologies need to be adapted to the specific needs of any industrial domain, e.g. railways, to facilitate the implementation and achievement of the overall business goals. This adaptation is denoted as ‘Industrial AI’(IAI). IAI for railways infrastructure and rolling stock, is dependent on an appropriate technology roadmap reflecting necessary know-hows. The IAI roadmap aims to provide a strategic and executive plan to augment managing railway assets i.e. ‘Augmented Asset Management (AAM)’. AAM can be applied through an end-to-end secure platform for e.g. data sharing among stakeholders, the development of analytics, and model sharing through distributed computing. AAM in railways can be enhanced through implementation of a generic fleet management (FM) approach. In the FM approach, any population of assets with common characteristics and also the relationship of the asset to the fleet is considered. This paper aims to develop and propose a concept for AAM enabled through IAI and digital technologies to provide augmented decision support through a secure platform, for AM in railways. A FM approach towards a holistic operation and maintenance of assets, based on a System of Systems thinking, for AAM in railways is applied for population of infrastructure assets and rolling stock assets with common characteristics. Finally, a taxonomy of issues and challenges, in the application of AAM to FM in railways is provided. The data for this taxonomy has been collected from railway organizations through iterative rounds of interviews. This taxonomy can be used for research and development of frameworks, approaches, technologies, and methodologies for AAM in railways.

Place, publisher, year, edition, pages
Sage Publications, 2022. Vol. 236, no 7, p. 850-862
Keywords [en]
Asset management in railways, maintenance in railways, fleet management in railways, augmented asset management, decision support systems, railways, rolling stock, Industrial Artificial Intelligence
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-87142DOI: 10.1177/09544097211045782ISI: 000695296200001Scopus ID: 2-s2.0-85114879353OAI: oai:DiVA.org:ltu-87142DiVA, id: diva2:1595558
Projects
AIFR (AI Factory for Railways)
Funder
VinnovaLuleå Railway Research Centre (JVTC)Swedish Transport Administration
Note

Validerad;2022;Nivå 2;2022-08-18 (sofila);

Funder: Alstom; Tågföretagen; Norrtåg; Infranord; Trasnitio; Bombardier; Sweco; Omicold; Damill and partners

Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2022-08-18Bibliographically approved
In thesis
1. Augmented Asset Management of Railway System Empowered by Industrial AI
Open this publication in new window or tab >>Augmented Asset Management of Railway System Empowered by Industrial AI
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-90416 (URN)978-91-8048-090-1 (ISBN)978-91-8048-091-8 (ISBN)
Presentation
2022-06-09, F1031, Luleå University of Technology, Luleå, 09:49 (English)
Opponent
Supervisors
Funder
Luleå Railway Research Centre (JVTC), 1675611
Available from: 2022-04-25 Created: 2022-04-25 Last updated: 2022-05-23Bibliographically approved

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Kumari, JayaKarim, RaminThaduri, AdithyaCastano, Miguel

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