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Publications (10 of 289) Show all publications
Imani, R., Chouhan, S., Putaala, J., Nousiainen, O., Hagberg, J., Myllymäki, S., . . . Delsing, J. (2023). A Fully Additive Fabrication Approach for sub-10-Micrometer Microvia Suitable for 3-D System-in-Package Integration. In: Proceedings - IEEE 73rd Electronic Components and Technology Conference, ECTC 2023: . Paper presented at 73rd IEEE Electronic Components and Technology Conference, ECTC 2023, Orlando, United States, May 30 - June 2, 2023 (pp. 1926-1931). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>A Fully Additive Fabrication Approach for sub-10-Micrometer Microvia Suitable for 3-D System-in-Package Integration
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2023 (English)In: Proceedings - IEEE 73rd Electronic Components and Technology Conference, ECTC 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 1926-1931Conference paper, Published paper (Refereed)
Abstract [en]

The semiconductor industry demands high input/output (I/O) density, requiring sub-l0-micrometer microvia. Here we propose a novel, fully additive, economical approach for creating and copper plating of microvias. The experimental process consisted of three stages. In Stage I, a polyurethane layer was spin-coated onto a FR-4 PCB base, followed by target copper layer deposition using the sequential build-up-covalent bonded metallization (SBU -CBM) method. In Stage II, first another layer of polyurethane was spin-coated on the top of the target copper layer, and then a microvia was created on the polyurethane layer using a picosecond pulsed ultraviolet (UV) laser. Finally, in Stage III, the SBU-CBM method was used to selectively copper plating of the microvia. Optical microscopy and cross-section scanning electron microscopy (SEM) images confirmed the successful formation and copper plating of sub-l0 micrometer microvia.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Series
Proceedings - Electronic Components Conference, ISSN 0569-5503, E-ISSN 2377-5726
Keywords
additive manufacturing, copper plating, microvia, picosecond pulsed ultraviolet laser
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-101103 (URN)10.1109/ECTC51909.2023.00331 (DOI)2-s2.0-85168309275 (Scopus ID)979-8-3503-3499-9 (ISBN)979-8-3503-3498-2 (ISBN)
Conference
73rd IEEE Electronic Components and Technology Conference, ECTC 2023, Orlando, United States, May 30 - June 2, 2023
Funder
Interreg Nord
Available from: 2023-08-30 Created: 2023-08-30 Last updated: 2023-08-30Bibliographically approved
Javed, S., Javed, S., van Deventer, J., Mokayed, H. & Delsing, J. (2023). A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge. In: Kemal Akkaya, Olivier Festor, Carol Fung, Mohammad Ashiqur Rahman, Lisandro Zambenedetti Granville, Carlos Raniery Paula dos Santos (Ed.), NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium: . Paper presented at IEEE/IFIP Network Operations and Management Symposium, May 8–12, 2023, Miami, USA. IEEE
Open this publication in new window or tab >>A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge
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2023 (English)In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium / [ed] Kemal Akkaya, Olivier Festor, Carol Fung, Mohammad Ashiqur Rahman, Lisandro Zambenedetti Granville, Carlos Raniery Paula dos Santos, IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE/IFIP Network Operations and Management Symposium, ISSN 1542-1201, E-ISSN 2374-9709
Keywords
Industry 5.0, Smart Manufacturing Ecosystem, Eclipse Arrowhead Framework, Value-driven Automation, Local Cloud-based Architecture, AI at the Edge, Collaborative Learning
National Category
Other Mechanical Engineering
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
urn:nbn:se:ltu:diva-96939 (URN)10.1109/NOMS56928.2023.10154323 (DOI)2-s2.0-85164738175 (Scopus ID)978-1-6654-7717-8 (ISBN)978-1-6654-7716-1 (ISBN)
Conference
IEEE/IFIP Network Operations and Management Symposium, May 8–12, 2023, Miami, USA
Note

European Commission, Arrowhead Tools project (ECSEL JU, No.826452)

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2023-10-11Bibliographically approved
Javed, S., Tripathy, A., van Deventer, J., Mokayed, H., Paniagua, C. & Delsing, J. (2023). An approach towards demand response optimization at the edge in smart energy systems using local clouds. Smart Energy, 12, Article ID 100123.
Open this publication in new window or tab >>An approach towards demand response optimization at the edge in smart energy systems using local clouds
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2023 (English)In: Smart Energy, ISSN 2666-9552, Vol. 12, article id 100123Article in journal (Refereed) Published
Abstract [en]

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Demand response optimization, Smart energy systems, AI at the edge, Local cloud-based architecture, Eclipse arrowhead framework, Industry 4.0, Industry 5.0
National Category
Energy Systems Computer Sciences
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
urn:nbn:se:ltu:diva-96933 (URN)10.1016/j.segy.2023.100123 (DOI)
Funder
European Commission, 101111977
Note

Validerad;2023;Nivå 2;2023-11-22 (hanlid);

Funder: Arrowhead flexible Production Value Network (fPVN) (101111977); AI-REDGIO5.0; 

Full text license: CC BY-NC-ND

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2023-11-22Bibliographically approved
Nilsson, M., Schelén, O., Lindgren, A., Bodin, U., Paniagua, C., Delsing, J. & Sandin, F. (2023). Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions. Frontiers in Neuroscience, 17, Article ID 1074439.
Open this publication in new window or tab >>Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions
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2023 (English)In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 17, article id 1074439Article in journal (Refereed) Published
Abstract [en]

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
neuromorphic computing, edge intelligence, event-driven systems, non-von Neumann, system integration, microservices, extreme heterogeneity, interoperability
National Category
Computer Systems Software Engineering Social Sciences Interdisciplinary
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
urn:nbn:se:ltu:diva-93711 (URN)10.3389/fnins.2023.1074439 (DOI)
Projects
Arrowhead ToolsDAISAI@Edge
Funder
The Kempe Foundations, JCK- 1809EU, Horizon Europe, 101015922
Note

Validerad;2023;Nivå 2;2023-02-17 (joosat);

Funder: ECSEL JU (737 459); KDT JU (101007273); ERUF Interreg Nord, (NYPS 20202460)

Licens fulltext: CC BY License

This article has previously appeared as a manuscript in a thesis.

Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2023-09-05Bibliographically approved
Garcia Represa, J., Larrinaga, F., Varga, P., Ochoa, W., Perez, A., Kozma, D. & Delsing, J. (2023). Investigation of Microservice-Based Workflow Management Solutions for Industrial Automation. Applied Sciences, 13(3), Article ID 1835.
Open this publication in new window or tab >>Investigation of Microservice-Based Workflow Management Solutions for Industrial Automation
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 3, article id 1835Article in journal (Refereed) Published
Abstract [en]

In an era ruled by data and information, engineers need new tools to cope with the increased complexity of industrial operations. New architectural models for industry enable open communication environments, where workflows can play a major role in providing flexible and dynamic interactions between systems. Workflows help engineers maintain precise control over their factory equipment and Information Technology (IT) services, from the initial design stages to plant operations. The current application of workflows departs from the classic business workflows that focus on office automation systems in favor of a manufacturing-oriented approach that involves direct interaction with cyber-physical systems (CPSs) on the shop floor. This paper identifies relevant industry-related challenges that hinder the adoption of workflow technology, which are classified within the context of a cohesive workflow lifecycle. The classification compares the various workflow management solutions and systems used to monitor and execute workflows. These solutions have been developed alongside the Eclipse Arrowhead framework, which provides a common infrastructure for designing systems according to the microservice architectural principles. This paper investigates and compares various solutions for workflow management and execution in light of the associated industrial requirements. Further, it compares various microservice-based approaches and their implementation. The objective is to support industrial stakeholders in their decision-making with regard to choosing among workflow management solutions.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
Arrowhead framework, business process management, microservices, service-oriented architecture, web service automation, workflow execution, workflow management
National Category
Computer Sciences
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-95763 (URN)10.3390/app13031835 (DOI)2-s2.0-85147989802 (Scopus ID)
Funder
European Commission, 826452
Note

Validerad;2023;Nivå 2;2023-03-02 (joosat);

Licens fulltext: CC BY License

Available from: 2023-03-02 Created: 2023-03-02 Last updated: 2023-03-02Bibliographically approved
Garcia Represa, J. & Delsing, J. (2023). Manufacturing workflows in microservice architectures supporting digital transactions for business process automation. In: Chin-Yin Huang, Rob Dekkers, Shun Fung Chiu, Daniela Popescu, Luis Quezada (Ed.), Intelligent and Transformative Production in Pandemic Times: Proceedings of the 26th International Conference on Production Research: . Paper presented at 26th International Conference on Production Research (ICPR), Taichung, Taiwan, July 18-21, 2021. (pp. 113-126). Springer, 1
Open this publication in new window or tab >>Manufacturing workflows in microservice architectures supporting digital transactions for business process automation
2023 (English)In: Intelligent and Transformative Production in Pandemic Times: Proceedings of the 26th International Conference on Production Research / [ed] Chin-Yin Huang, Rob Dekkers, Shun Fung Chiu, Daniela Popescu, Luis Quezada, Springer, 2023, Vol. 1, p. 113-126Conference paper, Published paper (Refereed)
Abstract [en]

Manufacturing systems are in the middle of a digital transformation. As systems in the assembly line are upgraded into cyber-physical systems (CPSs), capable of communicating between each other and carrying out complex computational tasks, the need for tight centralized control from an enterprise resource planning (ERP) and manufacturing execution system (MES) is less vital. In fact, not only manufacturing processes follow the trend toward decentralization and are moved to the edge layer. Other business processes along the supply chain have the potential to follow the digitalization process, such as procurement and supply flow management. This evolution brings new opportunities and challenges to the field. On the opportunity side, we identify shorter cycle times from product design to production, flexible production systems and multi-stakeholder production. Among the associated challenges, the collaboration of product, production, and business aware edge assets in multi-stakeholder environments stands out. This work proposes a new architecture for smart factories, in an environment where the products, supply stations and manufacturing equipment are controlled by different stakeholders. Requested manufacturing operations and supply flow are generated from machine-to-machine (M2M) negotiated business agreements between pairs of involved stakeholders. The manufacturing workflows are created and managed at each production workstation based on the smart product’s needs. Operations and supply flow progress is logged in distributed ledgers for the involved pairs of stakeholders, providing non-repudiation and immutable data on the M2M business agreement. The proposed architecture enables the automation of business processes providing benefits in terms of decreased transaction time and cost.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Production Engineering, ISSN 2194-0525, E-ISSN 2194-0533
Keywords
Workflow management, Service oriented architecture, Smart factories, Arrowhead framework
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-89397 (URN)10.1007/978-3-031-18641-7_12 (DOI)978-3-031-18640-0 (ISBN)978-3-031-18641-7 (ISBN)
Conference
26th International Conference on Production Research (ICPR), Taichung, Taiwan, July 18-21, 2021.
Projects
Arrowhead Tools
Funder
European Commission, 826452
Note

Funder: Productive 4.0 (Grant no. 737459)

Available from: 2022-02-26 Created: 2022-02-26 Last updated: 2023-08-14Bibliographically approved
Imani, R., Chouhan, S. S., Delsing, J. & Acharya, S. (2022). A Fully Additive Approach for the Fabrication of Split-Ring Resonator Metasurfaces. In: Proceedings: IEEE 72nd Electronic Components and Technology Conference (ECTC 2022): . Paper presented at 2022 IEEE 72nd Electronic Components and Technology Conference (ECTC), San Diego, USA, May 31 - June 3, 2022 (pp. 1834-1840). IEEE
Open this publication in new window or tab >>A Fully Additive Approach for the Fabrication of Split-Ring Resonator Metasurfaces
2022 (English)In: Proceedings: IEEE 72nd Electronic Components and Technology Conference (ECTC 2022), IEEE, 2022, p. 1834-1840Conference paper, Published paper (Other academic)
Abstract [en]

Metasurfaces, as a two-dimensional (2D) form of metamaterial, offer the possibility of designing miniaturized antennas for radio frequency (RF) energy harvesting systems with high efficiency, but fabrication of these antennas is still a major challenge. Printed circuit board (PCB) lithography, utilizing subtractive etch-and-print techniques to create metal interconnects on PCBs, was the first technique used to create metasurfaces antennas and remains the dominant technique to this day. The development of large-area fabrication techniques that are flexible, precise, uniform, cost-effective, and environmentally friendly is urgently needed for creating next-generation metasurfaces antenna. The present study reports a new fully additive manufacturing method for the fabrication of copper split-ring resonator (SRR) arrays on a PCB as a planar compact metasurfaces antenna. This new method was developed by combining sequential build up (SBU), laser direct writing (LDW), and covalent bonded metallization (CBM) methods and called (SBU-CBM). In this method, standard FR-4 covered with a layer of polyurethane was used as a basic PCB. The polymer surface was coated with a grafting molecule, followed by LDW to pattern the SRR array on the PCB. Finally, in electroless plating, only the laser-scanned area was selectively plated, and copper covalent bond metallization was selectively plated on the SRR pattern. Copper SRR arrays with different sizes were successfully fabricated on PCB using the SBU-CBM method. Copper strip lines within the SRR repeating building block were miniaturized up to 5 μm. To the best of our knowledge, this is the smallest size of a PCB antenna that has been reported to date.

Place, publisher, year, edition, pages
IEEE, 2022
Series
Electronic Components and Technology Conference (ECTC), ISSN 0569-5503, E-ISSN 2377-5726
Keywords
metamaterial, RF-energy harvesting, metasurface antenna, copper split-ring resonator, additive manufacturing, laser direct writing, electroless copper plating
National Category
Analytical Chemistry Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-92164 (URN)10.1109/ECTC51906.2022.00288 (DOI)000848765300281 ()2-s2.0-85134654273 (Scopus ID)978-1-6654-7943-1 (ISBN)
Conference
2022 IEEE 72nd Electronic Components and Technology Conference (ECTC), San Diego, USA, May 31 - June 3, 2022
Note

Funder: InterregNord-COMPACT

Available from: 2022-07-14 Created: 2022-07-14 Last updated: 2022-09-23Bibliographically approved
Urgese, G., Azzoni, P., van Deventer, J., Delsing, J., Macii, A. & Macii, E. (2022). A SOA-Based Engineering Process Model for the Life Cycle Management of System-of-Systems in Industry 4.0. Applied Sciences, 12(15), Article ID 7730.
Open this publication in new window or tab >>A SOA-Based Engineering Process Model for the Life Cycle Management of System-of-Systems in Industry 4.0
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 15, article id 7730Article in journal (Refereed) Published
Abstract [en]

The evolution of industrial digitalisation has accelerated in recent years with the availability of hyperconnectivity, low-cost miniaturised electronic components, edge computing, and Internet of Things (IoT) technologies. More generally, with these key enablers, the concept of a system of systems (SoS) is becoming a reality in the industry domain. However, due to its complexity, the engineering process model adopted to design, develop, and manage IoT and SoS-based solutions for industry digitalisation is inadequate, inefficient, and frequently unable to manage the digitalisation solution’s entire life cycle. To address these limitations, we propose the Arrowhead Engineering Process (Arrowhead-EP) model and the Value Chain Engineering Process Map (VCEP-map), which explicitly reveal the interactions and dynamics of the engineering processes adopted by multistakeholder use cases in the industry domain. We decomposed and remodeled the engineering process to cover the complete life cycle of an industrial SoS, and we introduced a service-oriented solution intended to efficiently, flexibly, and effectively manage the three assets addressed by RAMI 4.0. The Arrowhead-EP model complemented by the VCEP-map fills the gaps identified in our literature-based analysis and satisfies the requirements of the life cycle management of a typical use case in the Industry 4.0 domain. In this regard, a specific example is used to illustrate the advantages of adopting the proposed engineering solution in a real multistakeholder use case.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
engineering process model, system life cycle management, service oriented architecture (SOA), SOA/microservice, system of systems (SoS), Eclipse Arrowhead framework, Internet of Things (IoT), Industry 4.0
National Category
Computer Sciences Information Systems
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-92799 (URN)10.3390/app12157730 (DOI)000839314800001 ()2-s2.0-85136912872 (Scopus ID)
Projects
Arrowhead Tools
Funder
EU, Horizon 2020, 826452
Note

Validerad;2022;Nivå 2;2022-09-06 (hanlid)

Available from: 2022-09-06 Created: 2022-09-06 Last updated: 2022-09-12Bibliographically approved
Paniagua, C. & Delsing, J. (2022). Autonomous runtime consumer interface generation and deployment for service interoperability. Journal of Industrial Information Integration, 28, Article ID 100355.
Open this publication in new window or tab >>Autonomous runtime consumer interface generation and deployment for service interoperability
2022 (English)In: Journal of Industrial Information Integration, ISSN 2467-964X, E-ISSN 2452-414X, Vol. 28, article id 100355Article in journal (Refereed) Published
Abstract [en]

The new Industry 4.0 approach contributes to addressing evolving industrial requirements, which are continuously fueled by changing market demands. This situation leads to growing complexity and considerable increases in development and maintenance costs. A significant portion of engineering time is dedicated to the integration and interconnection of heterogeneous components. The solution for interoperability issues and the reduction in the associated engineering time are thus key tasks for increasing productivity and efficiency. Therefore, this paper provides an engineering approach to create interoperability among heterogeneous systems in Service Oriented Architecture (SOA) based environments by means of generating an autonomous consumer interface code at runtime.

This paper aims to present a novel interoperability solution. The proposed approach makes use of service interface descriptions to dynamically instantiate a new autonomously generated interface that solves service mismatches between a provider and a consumer. This paper includes the definition of the consumer interface generator system, as well as the benefits and challenges associated with the autonomous generation and deployment of a consumer interface code at runtime. To illustrate the potential of this approach, a prototype of the system, which shows positive results, is implemented and tested.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Interoperability, Industrial IoT, SOA, SoS, Code generation, Industry 4.0
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-90463 (URN)10.1016/j.jii.2022.100355 (DOI)000800377800007 ()2-s2.0-85129969240 (Scopus ID)
Note

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

Available from: 2022-04-28 Created: 2022-04-28 Last updated: 2023-05-08Bibliographically approved
Javed, S., Javed, S., van Deventer, J., Sandin, F., Delsing, J., Liwicki, M. & Martin del Campo Barraza, S. (2022). Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms. In: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS): . Paper presented at 5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2022), Coventry, United Kingdom, May 24-26, 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms
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2022 (English)In: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

Modeling Industrial Internet of Things (IIoT) architectures for the automation of wind turbines and farms(WT/F), as well as their condition monitoring (CM) is a growing concept among researchers. Several end-to-end automated cloud-based solutions that digitize CM operations intelligently to reduce manual efforts and costs are being developed. However, establishing robust and secure communication across WT/F is still difficult for the wind energy industry. We propose a fully automated cloud-based collaborative learning (CCL) architecture using the Eclipse Arrowhead Framework and an unsupervised dictionary learning (USDL) CM approach. The scalability of the framework enabled digitization and collaboration across the WT/Fs. Collaborative learning is a novel approach that allows all WT/Fs to learn from each other in real-time. Each turbine has CCL based CM using USDL as micro-services that autonomously perform feature selection and failure prediction to optimize cost, computation, and resources. The fundamental essence of the USDA approach is to enhance the WT/F’s learning and accuracy. We use dictionary distances as a metric for analyzing the CM of WT in our proposed USDL approach. A dictionary indicates an anomaly if its distances increased from the dictionary computed at a healthy state of that WT. Using CCL, a WT/F learns all types of failures that could occur in a similar WT/F, predicts any machinery failure, and sends alerts to the technicians to ensure guaranteed proactive maintenance. The results of our research support the notion that when testing a turbine with dictionaries of all the other turbines, every dictionary converges to similar behavior and captures the fault that occurs in that turbine.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
ndustry 4.0, Cloud-based Architectures, Eclipse Arrowhead Framework, Machine Learning, Unsupervised Learning, Wind Turbine, Wind Farms, Condition Monitoring
National Category
Computer Sciences
Research subject
Machine Learning; Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-90195 (URN)10.1109/ICPS51978.2022.9816960 (DOI)2-s2.0-85135621043 (Scopus ID)
Conference
5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2022), Coventry, United Kingdom, May 24-26, 2022
Projects
Arrowhead Tools
Note

Funder: ECSEL JU (82645);

ISBN för värdpublikation: 978-1-6654-9770-1

Available from: 2022-04-13 Created: 2022-04-13 Last updated: 2023-09-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4133-3317

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