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Publications (10 of 211) Show all publications
Kumar, U., Karim, R., Galar, D. & Kour, R. (2024). Editorial. In: Kumar U.; Karim R.; Galar D.; Kour R. (Ed.), International Congress and Workshop on Industrial AI and eMaintenance 2023: (pp. v-vi). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Editorial
2024 (English)In: International Congress and Workshop on Industrial AI and eMaintenance 2023 / [ed] Kumar U.; Karim R.; Galar D.; Kour R., Springer Science and Business Media Deutschland GmbH , 2024, p. v-viChapter in book (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
Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103907 (URN)2-s2.0-85182009595 (Scopus ID)978-3-031-39618-2 (ISBN)978-3-031-39619-9 (ISBN)
Available from: 2024-01-24 Created: 2024-01-24 Last updated: 2024-01-24Bibliographically approved
Kumar, U., Karim, R., Galar, D. & Kour, R. (Eds.). (2024). International Congress and Workshop on Industrial AI and eMaintenance 2023. Paper presented at IAI: International Congress and Workshop on Industrial AI, Luleå, Sweden, 13-15 june, 2023. Springer
Open this publication in new window or tab >>International Congress and Workshop on Industrial AI and eMaintenance 2023
2024 (English)Conference proceedings (editor) (Refereed)
Place, publisher, year, edition, pages
Springer, 2024. p. 801
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Reliability and Maintenance
Research subject
Quality Technology and Logistics
Identifiers
urn:nbn:se:ltu:diva-103915 (URN)10.1007/978-3-031-39619-9 (DOI)978-3-031-39618-2 (ISBN)978-3-031-39619-9 (ISBN)
Conference
IAI: International Congress and Workshop on Industrial AI, Luleå, Sweden, 13-15 june, 2023
Available from: 2024-01-24 Created: 2024-01-24 Last updated: 2024-01-24Bibliographically approved
Vila-Forteza, M., Jimenez-Cortadi, A., Diez-Olivan, A., Seneviratne, D. & Galar-Pascual, D. (2023). Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Model. In: E. Juuso; D. Galar (Ed.), Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021: . Paper presented at Fifth International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, February 16-17, 2021 (pp. 153-165). Springer Nature
Open this publication in new window or tab >>Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump Using a Hybrid Model
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2023 (English)In: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021 / [ed] E. Juuso; D. Galar, Springer Nature, 2023, p. 153-165Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer Nature, 2023
Series
Lecture Notes in Mechanical Engineering (LNME), ISSN 2195-4356, E-ISSN 2195-4364
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-100740 (URN)10.1007/978-981-99-1988-8_12 (DOI)2-s2.0-85172197449 (Scopus ID)
Conference
Fifth International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, February 16-17, 2021
Note

ISBN för värdpublikation: 978-981-99-1987-1; 978-981-99-1988-8

Available from: 2023-08-28 Created: 2023-08-28 Last updated: 2023-10-11Bibliographically approved
Karim, R., Galar, D. & Kumar, U. (2023). AI Factory: Theories, Applications and Case Studies (1ed.). Taylor & Francis
Open this publication in new window or tab >>AI Factory: Theories, Applications and Case Studies
2023 (English)Book (Other academic)
Abstract [en]

This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors. Features:

• Presents a compendium of methodologies and technologies in industrial AI and digitalisation.

• Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation.

• Covers a broad range of academic and industrial issues within the field of asset management.

• Discusses the impact of Industry 4.0 in other sectors.

• Includes a dedicated chapter on real-time case studies.

This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.

Place, publisher, year, edition, pages
Taylor & Francis, 2023. p. 444 Edition: 1
Series
AI Factory: Theories, Applications and Case Studies
National Category
Computer Systems
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-99496 (URN)10.1201/9781003208686 (DOI)2-s2.0-85165345291 (Scopus ID)9781032077642 (ISBN)9781003208686 (ISBN)
Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-08-11Bibliographically approved
Gálvez, A., Seneviratne, D., Galar, D. & Juuso, E. (2023). Feature Assessment for a Hybrid Model. In: Esko Juuso & Diego Galar (Ed.), Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021: . Paper presented at Fifth International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, February 16-17, 2021 (pp. 43-58). Springer Nature
Open this publication in new window or tab >>Feature Assessment for a Hybrid Model
2023 (English)In: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021 / [ed] Esko Juuso & Diego Galar, Springer Nature, 2023, p. 43-58Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes an assessment of features orientated to improve the accuracy of a hybrid model (HyM) used for detecting faults in a heating, ventilation, and air conditioning (HVAC) system. The HyM combines data collected by sensors embedded in the system with data generated by a physics-based model of the HVAC. The physics-based model includes sensors embedded in the real system and virtual sensors to represent the behaviour of the system when a failure mode (FM) is simulated. This fusion leads to improved maintenance actions to reduce the number of failures and predict the behaviour of the system. HyM can lead to improved fault detection and diagnostics (FDD) processes of critical systems, but multiple fault detection models are sometimes inaccurate. The paper assesses features extracted from synthetic signals. The results of the assessment are used to improve the accuracy of a multiple fault detection model developed in previous research. The assessment of features comprises the following: (1) generation of run-to-failure data using the physics-based model of the HVAC system; the FMs simulated in this paper are dust in the air filter, degradation of the CO2 sensor, degradation of the evaporator fan, and variations in the compression rate of the cooling system; (2) identification of the individual features that strongly distinguish the FM; (3) analysis of how the features selected vary when components degrade.

Place, publisher, year, edition, pages
Springer Nature, 2023
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
Keywords
Diagnostics, Fault detection, Feature assessment, HVAC system
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103370 (URN)10.1007/978-981-99-1988-8_4 (DOI)2-s2.0-85172243752 (Scopus ID)
Conference
Fifth International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, February 16-17, 2021
Note

ISBN for host publication: 978-981-99-1987-1 (print), 978-981-99-1987-1 (electronic)

Available from: 2024-01-03 Created: 2024-01-03 Last updated: 2024-01-03Bibliographically approved
Juuso, E. & Galar, D. (2023). Preface. In: Esko Juuso; Diego Galar (Ed.), Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021: . Paper presented at The Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, Oulu, Finland, 16 February 2021 through 17 February 2021 (pp. v-vi). Springer
Open this publication in new window or tab >>Preface
2023 (English)In: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021 / [ed] Esko Juuso; Diego Galar, Springer, 2023, p. v-viConference paper (Other academic)
Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Mechanical Engineering (LNME), ISSN 2195-4356, E-ISSN 2195-4364
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-102405 (URN)2-s2.0-85172229904 (Scopus ID)978-981-99-1987-1 (ISBN)978-981-99-1988-8 (ISBN)
Conference
The Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, Oulu, Finland, 16 February 2021 through 17 February 2021
Available from: 2023-11-12 Created: 2023-11-12 Last updated: 2023-11-12Bibliographically approved
Juuso, E. & Galar, D. (Eds.). (2023). Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021. Paper presented at Fifth International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, February 16-17, 2021. Springer Nature
Open this publication in new window or tab >>Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021
2023 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for condition monitoring and risk management professionals from industry and science exchange knowledge, experiences and strengthen multidisciplinary network those in the field. This book will be of benefit to academia, and industry alike.

Place, publisher, year, edition, pages
Springer Nature, 2023. p. 165
Series
Lecture Notes in Mechanical Engineering (LNME), ISSN 2195-4356, E-ISSN 2195-4364
National Category
Other Civil Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-100744 (URN)10.1007/978-981-99-1988-8 (DOI)978-981-99-1987-1 (ISBN)978-981-99-1988-8 (ISBN)
Conference
Fifth International Conference on Maintenance, Condition Monitoring and Diagnostics (MCMD 2021), Online, February 16-17, 2021
Available from: 2023-08-28 Created: 2023-08-28 Last updated: 2023-08-28Bibliographically approved
Galar, D. & Kumar, U. (2023). Robotics and artificial intelligence (AI) for maintenance. In: Pasquale Daponte, Florentin Paladi (Ed.), Monitoring and Protection of Critical Infrastructure by Unmanned Systems: (pp. 206-223). IOS Press
Open this publication in new window or tab >>Robotics and artificial intelligence (AI) for maintenance
2023 (English)In: Monitoring and Protection of Critical Infrastructure by Unmanned Systems / [ed] Pasquale Daponte, Florentin Paladi, IOS Press , 2023, p. 206-223Chapter in book (Other academic)
Abstract [en]

This paper reviews the application of AI in maintenance and inspections. It gives an overview of the development of AVs and distant inspection operations for industrial assets using unmanned aerial vehicles (UAVs). It discusses the use of AVs in infrastructure inspection and explain the types of sensors used for these applications. It explains how autonomous robots, including drones, are currently used in various industrial settings for inspection and maintenance. The paper concludes by discussing the use of AI in predictive maintenance.

Place, publisher, year, edition, pages
IOS Press, 2023
Series
NATO Science for Peace and Security Series - D: Information and Communication Security, ISSN 1874-6268, E-ISSN 1879-8292 ; 63
Keywords
AI, Failure, Faults, Maintenance, Robotics, UAV
National Category
Robotics Computer Sciences
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-97069 (URN)10.3233/NICSP230016 (DOI)2-s2.0-85153842204 (Scopus ID)978-1-64368-376-8 (ISBN)978-1-64368-377-5 (ISBN)
Available from: 2023-05-10 Created: 2023-05-10 Last updated: 2023-05-10Bibliographically approved
Vila Forteza, M., Galar Pascual, D., Kumar, U. & Verma, A. (2023). Work-In-Progress: Reliability Prediction of Api Centrifugal Pumps Using Survival Analysis. In: Zsolt János Viharos; Lorenzo Ciani; Piotr Bilski (Ed.), 19th IMEKO TC10 Conference: “MACRO meets NANO in Measurement for Diagnostics, Optimization and Control”: Proceedings. Paper presented at 19th IMEKO TC10 Conference: "MACRO meets NANO in Measurement for Diagnostics, Optimization and Control", Delft, Netherlands, September 21-22, 2023 (pp. 116-121). International Measurement Confederation (IMEKO)
Open this publication in new window or tab >>Work-In-Progress: Reliability Prediction of Api Centrifugal Pumps Using Survival Analysis
2023 (English)In: 19th IMEKO TC10 Conference: “MACRO meets NANO in Measurement for Diagnostics, Optimization and Control”: Proceedings / [ed] Zsolt János Viharos; Lorenzo Ciani; Piotr Bilski, International Measurement Confederation (IMEKO) , 2023, p. 116-121Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
International Measurement Confederation (IMEKO), 2023
National Category
Computational Mathematics Reliability and Maintenance
Research subject
Operation and Maintenance Engineering
Identifiers
urn:nbn:se:ltu:diva-103369 (URN)10.21014/tc10-2023.018 (DOI)
Conference
19th IMEKO TC10 Conference: "MACRO meets NANO in Measurement for Diagnostics, Optimization and Control", Delft, Netherlands, September 21-22, 2023
Note

ISBN for host publication: 978-92-990090-4-8

Available from: 2023-12-20 Created: 2023-12-20 Last updated: 2023-12-20Bibliographically approved
Galvez, A., Galar, D. & Seneviratne, D. (2022). A Hybrid Model-Based Approach on Prognostics for Railway HVAC. IEEE Access, 10, 108117-108127
Open this publication in new window or tab >>A Hybrid Model-Based Approach on Prognostics for Railway HVAC
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 108117-108127Article in journal (Refereed) Published
Abstract [en]

Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components’ degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes; therefore, it is difficult to find PHM implemented in HVAC systems. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate run-to-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components’ health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter’s run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Prognostics and health management, hybrid modelling, deep learning, HVAC system, railway
National Category
Vehicle Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-93717 (URN)10.1109/ACCESS.2022.3211258 (DOI)000870212300001 ()2-s2.0-85140811123 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-11-07 (joosat);

Available from: 2022-10-25 Created: 2022-10-25 Last updated: 2022-11-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4107-0991

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