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Publications (10 of 12) Show all publications
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
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
Kour, R., Patwardhan, A., Karim, R., Dersin, P. & Kumari, J. (2022). A cybersecurity approach for improved system resilience. In: Maria Chiara Leva; Edoardo Patelli; Luca Podofillini; Simon Wilson (Ed.), Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022): . Paper presented at 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, August 28 - September 1, 2022 (pp. 2514-2521). Research Publishing Services
Open this publication in new window or tab >>A cybersecurity approach for improved system resilience
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2022 (English)In: Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022) / [ed] Maria Chiara Leva; Edoardo Patelli; Luca Podofillini; Simon Wilson, Research Publishing Services , 2022, p. 2514-2521Conference paper, Published paper (Refereed)
Abstract [en]

The ongoing digitalisation of industrial systems is bringing new challenges in managing, monitoring, and predicting the overall reliability performance. The overall reliability of a cyber-physical system, such as railways, is highly influenced by the level of resilience in its inherent digital items. The objective of this paper is to propose a systematic approach, based on an enhanced Cyber Kill Chain model, to improve the overall system resilience through monitoring and prediction. The proposed cybersecurity approach can be used to assess the future cyberattack penetration probabilities based on the present security controls. With the advancement in cybersecurity defensive controls, cyberattacks have continued to evolve through the exploitation of vulnerabilities within the cyber-physical systems. Assuming the possibility of a cyberattack it is necessary to select appropriate security controls so that this attack can be predicted, prevented, or detected before any catastrophic consequences to retain the resilience of the system. Insufficient cybersecurity in the context of cyber-physical systems, such as railways, might have a fatal effect on the whole system availability performance and sometimes may lead to safety risks. However, to improve the overall resilience of a cyber-physical system there is a need of a systematic approach to continuously monitor, predict, and manage the health of the system’s digital items with respect to security. Furthermore, the paper will provide a case-study description in railway sector, which has been used for the verification of the proposed approach.

Place, publisher, year, edition, pages
Research Publishing Services, 2022
Keywords
Railway, Cybersecurity, Cyber Kill Chain, System Resilience
National Category
Other Civil Engineering Embedded Systems
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93983 (URN)10.3850/978-981-18-5183-4_S13-03-586-cd (DOI)
Conference
32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, August 28 - September 1, 2022
Funder
Luleå Railway Research Centre (JVTC)
Note

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

Available from: 2022-11-09 Created: 2022-11-09 Last updated: 2023-09-05Bibliographically approved
Kour, R., Castaño, M., Karim, R., Patwardhan, A., Kumar, M. & Granström, R. (2022). A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study. Sustainability, 14(2), Article ID 936.
Open this publication in new window or tab >>A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study
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2022 (English)In: Sustainability, E-ISSN 2071-1050, ISSN 2071-1050, Vol. 14, no 2, article id 936Article in journal (Refereed) Published
Abstract [en]

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
railway, asset management, model, virtual reality, mixed reality, AI, HoloLens 2
National Category
Human Computer Interaction
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-88826 (URN)10.3390/su14020936 (DOI)000747034900001 ()2-s2.0-85122909534 (Scopus ID)
Funder
Vinnova, 2019-05140
Note

Validerad;2022;Nivå 2;2022-01-31 (johcin)

Available from: 2022-01-17 Created: 2022-01-17 Last updated: 2023-09-13Bibliographically 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
Patwardhan, A. (2022). Enablement of digital twins for railway overhead catenary system. (Licentiate dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Enablement of digital twins for railway overhead catenary system
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Railway has the potential to become one of the most sustainable mediums for passenger and freight transport. This is possible by continuous updates to the asset management regime supporting Prognostics and Health Management (PHM). Railway tracks and catenaries are linear assets, and their length plays a vital role in maintenance. Railway catenary does not present many failures as compared to the rail track, but the failures that occur do not give enough opportunity for quick recovery. These failures cause extensive time delays disrupting railways operations. Such situations can be handled better by updating the maintenance approach. The domain of maintenance explores possible tools, techniques, and technologies to retain and restore the systems. PHM is dependent on data acquisition and analytics to predict the future state of a system with the least possible divergence. In the case of railway catenary and many other domains, this new technology of data acquisition is Light Detection And Ranging (LiDAR) device-based spatial point cloud collection. Current methods of catenary inspection depend on contact-based methods of inspection of railway catenary and read signals from the pantograph and contact wire while ignoring the rest of the wires and surroundings. Locomotive-mounted LiDAR devices support the collection of spatial data in the form of point-cloud from all the surrounding equipment and environment. This point cloud data holds a large amount of information, waiting for algorithms and technologies to harness it. A Digital Twin (DT) is a virtual representation of a physical system or process, achieved through models and simulations and maintains bidirectional communication for progressive enrichment at both ends. A systems digital twin is exposed to all the same conditions virtually. Such a digital twin can be used to provide prognostics by varying factors such as time, malfunction in components of the system, and conditions in which the system operates. Railways is a multistakeholder domain that depends on many organisations to support smooth function. The development of digital twins depends on the understanding of the system, the availability of sensors to read the state and actuators to affect the system’s state. Enabling a digital twin depends on governance restrictions, business requirements and technological competence. A concrete step towards enablement of the digital twin is designing an architecture to accommodate the technical requirements of content management, processing and infrastructure while addressing railway operations' governance and business aspects.The main objective of this work is to develop and provide architecture and a platform for the enablement of a DT solution based on Artificial Intelligence (AI) and digital technologies aimed at PHM of railway catenary system. The main results of this thesis are i) analysis of content management and processing requirements for railway overhead catenary system ii) methodology for catenary point cloud data processing and information representation iii) architecture and infrastructure requirements for enablement of Digital Twin and iv) roadmap for digital twin enablement for PHM of railway overhead catenary system.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Railway catenary, Maintenance, eMaintenance, LiDAR, Point Cloud, Digital Twin, Software Architecture
National Category
Computer Systems
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-92656 (URN)978-91-8048-129-8 (ISBN)978-91-8048-130-4 (ISBN)
Presentation
2022-10-14, F-1031, Luleå University of Technology, Luleå, 09:30 (English)
Opponent
Supervisors
Available from: 2022-08-25 Created: 2022-08-24 Last updated: 2022-12-01Bibliographically approved
Patwardhan, A., Thaduri, A., Karim, R. & Castano, M. (2022). Federated Learning for Enablement of Digital Twin. Paper presented at 14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022), Tel-Aviv, Israel, 28-30 March, 2022. IFAC-PapersOnLine, 55(2), 114-119
Open this publication in new window or tab >>Federated Learning for Enablement of Digital Twin
2022 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 55, no 2, p. 114-119Article in journal (Refereed) Published
Abstract [en]

Creation, maintenance, and update of digital twins are costly and time-consuming mechanisms. The required effort can be optimized with the use of LiDAR technologies, which support the process of collecting data related to spatial information such as location, geometry, and position. Sharing such data in multi-stakeholder environments is hindered due to competition, confidentiality, and security requirements. Multi-stakeholder environments favor the use of decentralized creation and update mechanisms with reduced data exchange. Such mechanisms are facilitated by Federated Learning, where the learning process is performed at the data owner’s location. Two case studies are presented in this paper, where LiDAR is used to extract information from industrial equipment as a part of the creation of a digital twin.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Digital twin, federated learning, LiDAR, point cloud, railway catenary
National Category
Computer Sciences
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-90584 (URN)10.1016/j.ifacol.2022.04.179 (DOI)000800779500020 ()2-s2.0-85132159718 (Scopus ID)
Conference
14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022), Tel-Aviv, Israel, 28-30 March, 2022
Note

Godkänd;2022;Nivå 0;2022-05-09 (sofila);Konferensartikel i tidskrift

Available from: 2022-05-09 Created: 2022-05-09 Last updated: 2023-09-13Bibliographically approved
Kour, R., Karim, R., Patwardhan, A., Kumar, M., Eriksson, H. & Kumar, U. (2022). Metaverse for Intelligent Asset Management. In: 2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM): . Paper presented at 2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM 2022), Anand, Gujarat, India, December 13-15, 2022. IEEE
Open this publication in new window or tab >>Metaverse for Intelligent Asset Management
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2022 (English)In: 2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM), IEEE, 2022Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2022
National Category
Human Computer Interaction Computer Systems
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-99227 (URN)10.1109/ICMIAM56779.2022.10146891 (DOI)2-s2.0-85163860699 (Scopus ID)
Conference
2022 International Conference on Maintenance and Intelligent Asset Management (ICMIAM 2022), Anand, Gujarat, India, December 13-15, 2022
Funder
Luleå Railway Research Centre (JVTC)Luleå University of Technology
Note

Funder: AI Factory;

ISBN for host publication: 978-1-6654-6179-5

Available from: 2023-07-19 Created: 2023-07-19 Last updated: 2024-01-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7438-1008

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