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Publications (10 of 63) Show all publications
Candell, O., Hällqvist, R., Olsson, E., Fransson, T., Thaduri, A. & Karim, R. (2024). Cyber-Physical Asset Management of Air Vehicle System. In: International Congress and Workshop on Industrial AI and eMaintenance 2023: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15,2023 (pp. 679-692). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Cyber-Physical Asset Management of Air Vehicle System
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2024 (English)In: International Congress and Workshop on Industrial AI and eMaintenance 2023, Springer Science and Business Media Deutschland GmbH , 2024, p. 679-692Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Mechanical Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103887 (URN)10.1007/978-3-031-39619-9_50 (DOI)2-s2.0-85181982286 (Scopus ID)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15,2023
Funder
Vinnova
Available from: 2024-01-23 Created: 2024-01-23 Last updated: 2024-01-23Bibliographically approved
Thaduri, A. (2024). Digital Twin: Definitions, Classification, and Maturity. In: International Congress and Workshop on Industrial AI and eMaintenance 2023: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15, 2023 (pp. 585-599). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Digital Twin: Definitions, Classification, and Maturity
2024 (English)In: International Congress and Workshop on Industrial AI and eMaintenance 2023, Springer Science and Business Media Deutschland GmbH , 2024, p. 585-599Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Computer and Information Sciences Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103874 (URN)10.1007/978-3-031-39619-9_43 (DOI)2-s2.0-85181978221 (Scopus ID)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15, 2023
Available from: 2024-01-23 Created: 2024-01-23 Last updated: 2024-01-23Bibliographically approved
Patwardhan, A., Thaduri, A. & Karim, R. (2024). Point Cloud Data Augmentation for Linear Assets. In: International Congress and Workshop on Industrial AI and eMaintenance 2023: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15, 2023 (pp. 615-625). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Point Cloud Data Augmentation for Linear Assets
2024 (English)In: International Congress and Workshop on Industrial AI and eMaintenance 2023, Springer Science and Business Media Deutschland GmbH , 2024, p. 615-625Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Computer and Information Sciences Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103871 (URN)10.1007/978-3-031-39619-9_45 (DOI)2-s2.0-85181975804 (Scopus ID)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, June 13-15, 2023
Available from: 2024-01-23 Created: 2024-01-23 Last updated: 2024-01-23Bibliographically approved
Kasraei, A., Garmabaki, A. H., Odelius, J., Chamkhorami, K. S. & Thaduri, A. (2023). Climate change and its weather hazard on the reliability of railway infrastructure. In: Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023): . Paper presented at 33rd European Safety and Reliability Conference (ESREL 2023), Southampton, United Kingdom, September 3-7, 2023 (pp. 2072-2078). Research Publishing Services, Article ID P044.
Open this publication in new window or tab >>Climate change and its weather hazard on the reliability of railway infrastructure
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2023 (English)In: Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023), Research Publishing Services , 2023, p. 2072-2078, article id P044Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Research Publishing Services, 2023
National Category
Infrastructure Engineering Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103276 (URN)10.3850/978-981-18-8071-1_P044-cd (DOI)
Conference
33rd European Safety and Reliability Conference (ESREL 2023), Southampton, United Kingdom, September 3-7, 2023
Funder
Vinnova, 2021-02456, 2019-03181The Kempe Foundations, JCK-2215
Note

ISBN for host publication: 978-981-18-8071-1

Available from: 2023-12-08 Created: 2023-12-08 Last updated: 2024-01-29Bibliographically approved
Thaduri, A., Patwardhan, A., Kour, R. & Karim, R. (2023). Predictive maintenance of mobile mining machinery: A case study for dumpers. In: Mário P. Brito; Terje Aven; Piero Baraldi; Marko Čepin; Enrico Zio (Ed.), Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023): . Paper presented at 33rd European Safety and Reliability Conference (ESREL 2023), Southampton, United Kingdom, September 3-7, 2023 (pp. 3481-3488). Research Publishing Services, Article ID P298.
Open this publication in new window or tab >>Predictive maintenance of mobile mining machinery: A case study for dumpers
2023 (English)In: Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023) / [ed] Mário P. Brito; Terje Aven; Piero Baraldi; Marko Čepin; Enrico Zio, Research Publishing Services , 2023, p. 3481-3488, article id P298Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Research Publishing Services, 2023
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-101590 (URN)10.3850/978-981-18-8071-1_P298-cd (DOI)
Conference
33rd European Safety and Reliability Conference (ESREL 2023), Southampton, United Kingdom, September 3-7, 2023
Funder
Luleå University of Technology
Note

Funder: Coal India funding agency; AI Factory Platform;

ISBN for host publication: 978-981-18-8071-1

Available from: 2023-10-06 Created: 2023-10-06 Last updated: 2024-01-29Bibliographically approved
Kumari, J., Karim, R., Thaduri, A. & Dersin, P. (2022). A framework for now-casting and forecasting in augmented asset management. International Journal of Systems Assurance Engineering and Management, 13(5), 2640-2655
Open this publication in new window or tab >>A framework for now-casting and forecasting in augmented asset management
2022 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 13, no 5, p. 2640-2655Article in journal (Refereed) Published
Abstract [en]

Asset Management of a complex technical system-of-systems needs cross-organizational operation and maintenance, asset data management and context-aware analytics. Emerging technologies such as AI and digitalisation can facilitate the augmentation of asset management (AAM), by providing data-driven and model-driven approaches to analytics, i.e., now-casting and forecasting. However, implementing context-aware now-casting and forecasting analytics in an operational environment with varying contexts such as for fleets and distributed infrastructure is challenging. The number of algorithms in such an implementation can be vast due to the large number of assets and operational contexts for the fleet. To reduce the complexity of the analytics, it is required to optimize the number of algorithms. This can be done by optimizing the number of operational contexts through a generalization and specialization approach based on both fleet behaviour and individual behaviour for improved analytics. This paper proposes a framework for context-aware now-casting and forecasting analytics for AAM based on a top-down, i.e., Fleet2Individual and bottom-up, i.e., Individual2Fleet approach. The proposed framework has been described and verified by applying it to the context of railway rolling stock in Sweden. The benefits of the proposed framework is to provide industries with a tool that can be used to simplify the implementation of AI and digital technologies in now-casting and forecasting.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Now-casting, Forecasting, Asset management, Augmented asset management, Fleet management, Rolling stock
National Category
Reliability and Maintenance Computer Vision and Robotics (Autonomous Systems)
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-92157 (URN)10.1007/s13198-022-01721-2 (DOI)000821370800001 ()2-s2.0-85133591516 (Scopus ID)
Projects
AI Factory
Funder
VinnovaSwedish Transport Administration
Note

Validerad;2022;Nivå 2;2022-11-30 (sofila);

Funder: JVTC (Luleå Railway Research Center); Trafikverket; Alstom; Tågföretagen; Norrtåg; Infranord; Trasnitio; Bombardier; Sweco; Omicold and Damill

Available from: 2022-07-13 Created: 2022-07-13 Last updated: 2023-09-05Bibliographically approved
Kour, R., Patwardhan, A., Thaduri, A. & Karim, R. (2022). A Methodology for Cybersecurity Risk Assessment – A Case-study in Railway. International Journal of COMADEM, 25(2), 5-12
Open this publication in new window or tab >>A Methodology for Cybersecurity Risk Assessment – A Case-study in Railway
2022 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 25, no 2, p. 5-12Article in journal (Refereed) Published
Abstract [en]

Digitalisation is changing the railway globally. One of the major concerns in digital transformation of the railway is the increased exposure to cyberattacks. The railway is vulnerable to these cyberattacks because the number of digital items and number of interfaces between digital and physical components in these systems keep growing. Increased number of digital items and interfaces require new methodologies, frameworks, models, concepts, and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as adoption and convergence of Information Technology (IT) and Operational Technology (OT) technology within the railway. This convergence has brought significant benefits in reliability, operational efficiency, capacity as well as improvements in passenger experience but also increases the vulnerability towards cyberattacks from individuals, organizations, and governments. This paper proposes a methodology on how to deals with OT security in the railway signalling using failure mode, effects and criticality analysis (FMECA) and ISA/IEC 62443 security risk assessment methodologies.

Place, publisher, year, edition, pages
COMADEM International, 2022
Keywords
Operational security, ISA/IEC 62443, FMECA, railway, cyber threat, risk assessment
National Category
Computer Engineering Infrastructure Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-92981 (URN)2-s2.0-85168770411 (Scopus ID)
Funder
Luleå Railway Research Centre (JVTC)
Note

Validerad;2022;Nivå 1;2022-09-30 (hanlid)

Available from: 2022-09-12 Created: 2022-09-12 Last updated: 2023-09-28Bibliographically approved
Kour, R., Patwardhan, A., Thaduri, A. & Karim, R. (2022). A review on cybersecurity in railways. Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit
Open this publication in new window or tab >>A review on cybersecurity in railways
2022 (English)In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017Article, review/survey (Refereed) Published
Abstract [en]

Digitalisation is transforming the railway globally. One of the major considerations in digital transformation of any industry including the railway is the increased exposure to cyberattacks. The railway industry is vulnerable to these attacks because since the number of digital items and also number of interfaces between digital and physical components in the railway systems keep increasing. Increased number of items and interfaces require new frameworks, concepts and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as lack of proactiveness, lack of holistic perspective and obsolescence of safety systems exposed to current and future cyber threats landscape. To this date, there are several works carried out in the literature that studied the cybersecurity aspects and its application on railway infrastructure. However, to develop and implement an appropriate roadmap to cybersecurity in railways, there is a need of describing emerging challenges, and approaches to deal with these challenges and the possibilities and benefits of these.Hence, the objective of this paper is to provide a systematic review and outline cybersecurity emerging trends and approaches, and also to identify possible solutions by querying literature, academic and industrial, for future directions. The authors of this paper conducted separate searches through four popular databases, that is, Google Scholar, Scopus, Web of Science and IEEE explore. For the screening process, authors have used keywords with Boolean operators and database filters and identified 90 articles most relevant to the study domain. The analysis of 90 articles shows that majority of the cybersecurity studies lies within the railways are conceptual and lags in application of Artificial Intelligence (AI) based security. Like other industries, it is very important that railways should also follow latest security technologies, trends and train their workforce for cyber hygiene since railways are already in digitalization transition mode.

Place, publisher, year, edition, pages
Sage Publications, 2022
Keywords
cybersecurity, safety, railway, review, challenges
National Category
Robotics Transport Systems and Logistics
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-90448 (URN)10.1177/09544097221089389 (DOI)000798405900001 ()2-s2.0-85129455821 (Scopus ID)
Projects
AI Factory for Railways (AIF/R)
Funder
Vinnova
Note

Validerad;2022;Nivå 2;2022-06-02 (hanlid);

Funder: Luleå Railway Research Center, JVTC

Available from: 2022-04-27 Created: 2022-04-27 Last updated: 2022-08-24Bibliographically approved
Patwardhan, A., Thaduri, A., Karim, R. & Castano, M. (2022). An Architecture for Predictive Maintenance using 3D Imaging: A Case Study on Railway Overhead Catenary. In: Maria Chiara Leva; Edoardo Patelli; Luca Podofillini; Simon Wilson (Ed.), Proceedings of the 32nd EuropeanSafety and Reliability Conference (ESREL 2022): . Paper presented at 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, August 28 - September 1, 2022 (pp. 3103-3110). Research Publishing Services
Open this publication in new window or tab >>An Architecture for Predictive Maintenance using 3D Imaging: A Case Study on Railway Overhead Catenary
2022 (English)In: Proceedings of the 32nd EuropeanSafety and Reliability Conference (ESREL 2022) / [ed] Maria Chiara Leva; Edoardo Patelli; Luca Podofillini; Simon Wilson, Research Publishing Services, 2022, p. 3103-3110Conference paper, Published paper (Refereed)
Abstract [en]

Railway Overhead Catenary (ROC) system is critical for railways’ overall performance! ROC is a linear asset that is spread over a large geographical area. Insufficient performance of ROC has a significant impact on the overall railway operations, which leads to decreased availability and affects performance of the railway system. Prognostic and Health Management (PHM) of ROC is necessary to improve the dependability of the railway. PHM of ROC can be enhanced by implementing a data-driven approach. A data-driven approach to PHM is highly dependent on the availability and accessibility of data, data acquisition, processing and decision-support. Acquiring data for PHM of ROC can be used through various methods, such as manual inspections. Manual inspection of ROC is a time-consuming and costly method to assess the health of the ROC. Another approach for assessing the health of ROC is through condition monitoring using 3D scanning of ROC utilising LiDAR technology.Presently, 3D scanning systems like LiDAR scanners present new avenues for data acquisition of such physical assets. Large amounts of data can be collected from aerial, on-ground, and subterranean environments. Handling and processing this large amount of data require addressing multiple challenges like data collection, processing algorithms, information extraction, information representation, and decision support tools. Current approaches concentrate more on data processing but lack the maturity to support the end-to-end process. Hence, this paper investigates the requirements and proposes an architecture for a data-to-decision approach to PHM of ROC based on utilisation of LiDAR technology.

Place, publisher, year, edition, pages
Research Publishing Services, 2022
Keywords
Digital twin, Architecture, point cloud, railway catenary, maintenance
National Category
Computer Systems Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93579 (URN)10.3850/978-981-18-5183-4_S30-04-588-cd (DOI)
Conference
32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, August 28 - September 1, 2022
Projects
AIFR Project
Funder
VinnovaLuleå Railway Research Centre (JVTC)Swedish Transport Administration
Note

ISBN för värdpublikation: 978-981-18-5183-4

Available from: 2022-10-12 Created: 2022-10-12 Last updated: 2023-01-23Bibliographically approved
Kumari, J., Karim, R., Thaduri, A. & Castano, M. (2022). Augmented asset management in railways - Issues and challenges in rolling stock. Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, 236(7), 850-862
Open this publication in new window or tab >>Augmented asset management in railways - Issues and challenges in rolling stock
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
Keywords
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:nbn:se:ltu:diva-87142 (URN)10.1177/09544097211045782 (DOI)000695296200001 ()2-s2.0-85114879353 (Scopus ID)
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1938-0985

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