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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
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
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
Olsson, E., Candell, O., Funk, P., Sohlberg, R., Castaño, M., Gustafsson, M. A. & Bladh, P. (2022). Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations. In: ICAS Proceedings 33rd Congress of the International Council of the Aeronautical Sciences, Stockholm, Sweden: . Paper presented at 33rd Congress of the International Council of the Aeronautical Sciences (ICAS 2022), Stockholm, Sweden, September 4-9, 2022. International Council of the Aeronautical Sciences
Open this publication in new window or tab >>Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations
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2022 (English)In: ICAS Proceedings 33rd Congress of the International Council of the Aeronautical Sciences, Stockholm, Sweden, International Council of the Aeronautical Sciences , 2022Conference paper, Published paper (Refereed)
Abstract [en]

This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilizeda full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge,readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack.

Place, publisher, year, edition, pages
International Council of the Aeronautical Sciences, 2022
Keywords
Graph Database, Graph Algorithms, Interoperability, Aircraft Maintenance, Grand-Stack
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-97818 (URN)2-s2.0-85159708436 (Scopus ID)
Conference
33rd Congress of the International Council of the Aeronautical Sciences (ICAS 2022), Stockholm, Sweden, September 4-9, 2022
Funder
Vinnova
Available from: 2023-06-09 Created: 2023-06-09 Last updated: 2023-06-09Bibliographically approved
Castaño Arranz, M., Gustafson, A. & Al-Chalabi, H. (2020). A generic framework for data quality analytics. International Journal of COMADEM, 23(1), 31-38
Open this publication in new window or tab >>A generic framework for data quality analytics
2020 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 23, no 1, p. 31-38Article in journal (Refereed) Published
Abstract [en]

The challenge of generalizing Data Quality assessment is hindered by the fact that Data Quality requisites depend on the purpose for which the data will be used and on the subjectivity of the data consumer. The approach proposed in this paper to address this challenge is to employ a semi-automated user-guided Data Quality assessment. This paper introduces a generic framework for data quality analytics which is mainly composed by a set of software units to perform semi-automated Data Quality analytics and a set of Graphical User Interfaces to enable the user to guide the Data Quality assessment. The framework has been implemented and can be customized according to the needs of the purpose and of the consumer. The framework has been instantiated in a case study on Long-hole drill rigs, where several Data Quality issues have been discovered and their root cause investigated.

Place, publisher, year, edition, pages
COMADEM International, 2020
Keywords
Data Quality, Maintenance, Long-hole drilling, Mining
National Category
Other Civil Engineering
Research subject
Operation and Maintenance; Mining and Rock Engineering
Identifiers
urn:nbn:se:ltu:diva-78501 (URN)2-s2.0-85088900505 (Scopus ID)
Projects
IDQ4LCCAIF/R
Funder
Vinnova
Note

Validerad;2020;Nivå 1;2020-04-21 (alebob)

Available from: 2020-04-15 Created: 2020-04-15 Last updated: 2020-09-02Bibliographically approved
Mansouri, S. S., Pourkamali-Anaraki, F., Castano, M., Agha-mohammadi, A.-a., Burdick, J. & Nikolakopoulos, G. (2020). Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud. In: 2020 28th Mediterranean Conference on Control and Automation (MED): . Paper presented at 2020 28th Mediterranean Conference on Control and Automation (MED), 15-18 September, 2020, Saint-Raphaël, France (pp. 802-807). IEEE
Open this publication in new window or tab >>Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
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2020 (English)In: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, p. 802-807Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The  framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology. 

Place, publisher, year, edition, pages
IEEE, 2020
Series
Mediterranean Conference on Control and Automation (MED), ISSN 2325-369X, E-ISSN 2473-3504
National Category
Control Engineering Other Civil Engineering
Research subject
Control Engineering; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-79107 (URN)10.1109/MED48518.2020.9183337 (DOI)000612207700130 ()2-s2.0-85092158501 (Scopus ID)
Conference
2020 28th Mediterranean Conference on Control and Automation (MED), 15-18 September, 2020, Saint-Raphaël, France
Note

ISBN för värdpublikation: 978-1-7281-5742-9, 978-1-7281-5743-6

Available from: 2020-06-01 Created: 2020-06-01 Last updated: 2021-03-04Bibliographically approved
Kanellakis, C., Mansouri, S. S., Castaño, M., Karvelis, P., Kominiak, D. & Nikolakopoulos, G. (2020). Where to look: a collection of methods for MAV heading correction in underground tunnels. IET Image Processing, 14(10), 2020-2027
Open this publication in new window or tab >>Where to look: a collection of methods for MAV heading correction in underground tunnels
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2020 (Swedish)In: IET Image Processing, ISSN 1751-9659, E-ISSN 1751-9667, Vol. 14, no 10, p. 2020-2027Article in journal (Refereed) Published
Abstract [en]

Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)-c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.

Place, publisher, year, edition, pages
The Institution of Engineering and Technology, 2020
National Category
Robotics Other Civil Engineering
Research subject
Robotics and Artificial Intelligence; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-80881 (URN)10.1049/iet-ipr.2019.1423 (DOI)000583360400010 ()2-s2.0-85093857578 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-09-22 (johcin)

Available from: 2020-09-22 Created: 2020-09-22 Last updated: 2023-09-05Bibliographically approved
Mansouri, S. S., Castaño, M., Kanellakis, C. & Nikolakopoulos, G. (2019). Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection. In: Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros (Ed.), Computer Vision Systems: 12th International Conference, ICVS 2019 Thessaloniki, Greece, September 23–25, 2019 Proceedings. Paper presented at 12th International Conference on Computer Vision Systems (ICVS 2019), September 23–25, 2019, Thessaloniki, Greece (pp. 164-174). Springer
Open this publication in new window or tab >>Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection
2019 (English)In: Computer Vision Systems: 12th International Conference, ICVS 2019 Thessaloniki, Greece, September 23–25, 2019 Proceedings / [ed] Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros, Springer, 2019, p. 164-174Conference paper, Published paper (Refereed)
Abstract [en]

This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, efficient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu's threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the inherent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly autonomously through tunnel confined spaces.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11754
Keywords
Micro Aerial Vehicles (MAVs), Vision-based Navigation, Autonomous Drift Inspection, Otsu's Theshold, Moore-Neighborhood Tracing
National Category
Control Engineering Other Civil Engineering
Research subject
Control Engineering; Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-75270 (URN)10.1007/978-3-030-34995-0_16 (DOI)000548737700016 ()2-s2.0-85077010475 (Scopus ID)
Conference
12th International Conference on Computer Vision Systems (ICVS 2019), September 23–25, 2019, Thessaloniki, Greece
Funder
EU, Horizon 2020, 730302
Note

ISBN för värdpublikation: 978-3-030-34994-3, 978-3-030-34995-0

Available from: 2019-07-09 Created: 2019-07-09 Last updated: 2020-08-26Bibliographically approved
Tomás, D., Torres Farinha, J., Fonseca, I. & Arranz, M. C. (2019). Online Sensor and Industrial Systems Connecting Approach: A Global Review. In: Miguel Castaño Arranz, Ramin Karim (Ed.), Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications. Paper presented at 5th International Workshop and Congress on eMaintenance, 14-15 May, 2019, Stockholm, Sweden (pp. 57-62). Luleå University of Technology
Open this publication in new window or tab >>Online Sensor and Industrial Systems Connecting Approach: A Global Review
2019 (English)In: Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications / [ed] Miguel Castaño Arranz, Ramin Karim, Luleå University of Technology, 2019, p. 57-62Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this paper is to create a state-of-the-art review of the existing standards of open-source communications protocols which enables eMaintenance solutions in the industry. This review includes the explanation of Open O&M (Open Operations & Maintenance) that creates bridges between MIMOSA (Machinery Information Management Open Systems Alliance), the OPC Foundation (Open Protocol Communication), and the ISA SP95 (Instrumentation Systems and Automation Society's, SP95 Committee), in order to manage the problem under discussion. The understanding of the actual state-of-the-art reveals needs for further advancements of technologies and information standards for the exchange of Open O&M data and linked context. Furthermore, in order to be an active player in the industry, it is strategic to simplify the management and integration of enterprise information resources in a cooperating mode.

Place, publisher, year, edition, pages
Luleå University of Technology, 2019
Keywords
eMaintenance, CMMS, EAM, Interoperability, OSA-CBM
National Category
Reliability and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76831 (URN)
Conference
5th International Workshop and Congress on eMaintenance, 14-15 May, 2019, Stockholm, Sweden
Note

ISBN för värdpublikation: 978-91-7790-475-5

Available from: 2019-11-23 Created: 2019-11-23 Last updated: 2020-09-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9992-7791

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