Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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: 2024-03-20Bibliographically 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
2. A System-of-Systems Approach for Enhancing Asset Management of Railway System
Open this publication in new window or tab >>A System-of-Systems Approach for Enhancing Asset Management of Railway System
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In Sweden, railway transport of freight and passengers is a significant portion of the total transport system. The demand for railway transport is forecasted to increase in the coming decades. One of the main reasons for this ever-increasing demand is the requirement for sustainable transport, nationally and globally. Today, railways are considered an environment-friendly option of transport. The increasing demands on railway transport raise the requirements on the efficiency and effectiveness of the railway system.

From a system engineering perspective, the railway system is generally described to consist of two (2) main systems, i.e. a) railway infrastructure and b) railway rolling stock. Further, each of these two systems consists of a set of inherent interconnected integrated systems. Hence, from a system engineering perspective, the railway system can be considered as a System-of-Systems (SoS). Managing complex technical SoS, such as the railway system and its inherent items (also considered as assets), is complex and complicated, that requires a holistic systemic and systematic approach for asset management.

A holistic and systematic asset management strategy, considering aspects of reliability, availability, maintainability, safety, and security, is essential in ensuring railway system proficiency. This SoS approach will enforce fact-based informed decision-making by enabling a comprehensive understanding of assets within interconnected systems, facilitating strategic, tactical, and operative planning and execution decision-making as well as tactical processes and operational activities. Augmenting asset management with data-driven analytics, with a focus on the maintenance of assets, is expected to improve the effectiveness and efficiency of asset management. However, challenges related to data quality issues and dynamic asset characteristics must be addressed to gain the anticipated benefits of digitalisation.

Asset management of railway infrastructure has received substantial attention from within academia and industry. However, there is a noticeable research gap in the field of railway rolling stock asset management. The characteristics of the railway rolling stock system such as cross-organisational operation and maintenance, and the aspects of fleet management, poses certain challenges. These challenges are related to factors such as 1) the selection of maintenance strategies, 2) considering the dynamic nature of maintenance decisions and strategies 3) a holistic approach to increase system availability, and 4) the use of data-driven approaches such as industrial artificial intelligence, now-casting and forecasting.

To address these challenges and bridge the gaps, there is a need to identify the state-of-the-art and challenges associated with asset management of railway rolling stock. Additionally, there is a need to develop a holistic, systemic, and dynamic approach utilising data-driven solutions for enhanced asset management of railway rolling stock. The development of such an approach requires frameworks, tools, technologies, methodologies, and tools. These artefacts will also increase the knowledge related to domain requirements, state-of-the-art, best practices, and use of technology in asset management of railway rolling stock.

Hence, in this research, a taxonomy of issues and challenges has been identified. Furthermore, additional artefacts such as approaches, frameworks, platforms, technologies, methodologies, and tools for asset management of railway rolling stock have been developed and provided. These artefacts have been developed through literature surveys, experiments, best practices, standards, structured and semi-structured interviews with experienced professionals from railway organisations and learning from the development of demonstrators in the context of asset management and maintenance of railway rolling stock.

These developed and provided artefacts utilising an SoS approach can be used to establish effective and efficient asset management of railway rolling stock with a focus on the use of Industrial AI and digitalisation for the improvement of operation and maintenance processes. These artefacts can also be used by railway organisations to enhance the existing asset management and maintenance processes for railway rolling stock.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
railways rolling stock, system-of-systems, asset management, maintenance, fleet management, industrial AI, digitalisation
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-104690 (URN)978-91-8048-509-8 (ISBN)978-91-8048-510-4 (ISBN)
Public defence
2024-05-15, C305, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Projects
JVTC, AI Factory for railways
Available from: 2024-03-21 Created: 2024-03-20 Last updated: 2024-04-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Kumari, JayaKarim, RaminThaduri, AdithyaCastano, Miguel

Search in DiVA

By author/editor
Kumari, JayaKarim, RaminThaduri, AdithyaCastano, Miguel
By organisation
Operation, Maintenance and Acoustics
In the same journal
Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit
Other Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 334 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf