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Publications (10 of 73) Show all publications
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
Wintercorn, O., Atta, K., Jeppsson, P., Paniagua, C. & van Deventer, J. (2023). Transforming Brownfield Factories: Unleashing the Potential with Co-Engineering and Virtual Commissioning. In: IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society: . Paper presented at 49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), Singapore, Singapore, October 16-19, 2023. IEEE
Open this publication in new window or tab >>Transforming Brownfield Factories: Unleashing the Potential with Co-Engineering and Virtual Commissioning
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2023 (English)In: IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2023Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
Series
Annual Conference of Industrial Electronics Society
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Research subject
Cyber-Physical Systems; Automatic Control; Machine Design
Identifiers
urn:nbn:se:ltu:diva-102538 (URN)10.1109/IECON51785.2023.10311670 (DOI)
Conference
49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023), Singapore, Singapore, October 16-19, 2023
Note

ISBN for host publication: 979-8-3503-3183-7, 979-8-3503-3182-0

Available from: 2023-11-21 Created: 2023-11-21 Last updated: 2023-12-14Bibliographically 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
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
Javed, S., Tripathy, A., van Deventer, J., Paniagua, C., Patil, S. & Delsing, J. (2022). Demand Response in Distributed Energy Systems of Systems Using Local-Cloud: An Approach towards Net-Zero Emissions. In: Henrik Lund (Ed.), 8th International Conference on Smart Energy Systems13-14 September 2022: Book of Abstracts. Paper presented at 8th International Conference on Smart Energy Systems, Aalborg, Denmark, September 13-14, 2022 (pp. 59-60). Aalborg Universitetsforlag
Open this publication in new window or tab >>Demand Response in Distributed Energy Systems of Systems Using Local-Cloud: An Approach towards Net-Zero Emissions
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2022 (English)In: 8th International Conference on Smart Energy Systems13-14 September 2022: Book of Abstracts / [ed] Henrik Lund, Aalborg Universitetsforlag, 2022, p. 59-60Conference paper, Oral presentation with published abstract (Refereed)
Place, publisher, year, edition, pages
Aalborg Universitetsforlag, 2022
Keywords
Demand Response, Eclipse Arrowhead Framework, Local Cloud, Net-Zero Emission, Industry 5.0, Z-Wave, Microservices
National Category
Energy Systems
Research subject
Cyber-Physical Systems; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-93729 (URN)
Conference
8th International Conference on Smart Energy Systems, Aalborg, Denmark, September 13-14, 2022
Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2023-11-08Bibliographically approved
Tripathy, A., van Deventer, J., Paniagua, C. & Delsing, J. (2022). Interoperability Between ROS and OPC UA: A Local Cloud-Based Approach. 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), Coventry, United Kingdom, May 24-26, 2022. IEEE
Open this publication in new window or tab >>Interoperability Between ROS and OPC UA: A Local Cloud-Based Approach
2022 (English)In: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), IEEE, 2022Conference paper, Published paper (Other academic)
Abstract [en]

Today’s manufacturing industries use a large suite of protocols and technologies to operate heterogeneous devices and software modules. Some of the most widely used technologies in industrial production are OPC UA (Open Platform CommunicationsUnified Architecture) and ROS (Robot Operating System). Hence, enabling interoperability across these technologies is critical to ensure a smooth production flow. We propose a local cloud-based approach to achieve interoperability between ROSand OPC UA by integrating them with the Eclipse ArrowheadFramework. This integration allows these technologies to operate as independent systems while communicating securely at runtime. In addition to achieving interoperability, this integration supports important industrial aspects such as loose coupling, late binding, and cyber-security, making it a flexible solution.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
ROS, OPC UA, Eclipse Arrowhead Framework
National Category
Computer Sciences
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-90999 (URN)10.1109/ICPS51978.2022.9816962 (DOI)2-s2.0-85135624724 (Scopus ID)
Conference
5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, United Kingdom, May 24-26, 2022
Note

ISBN for host publication: 978-1-6654-9770-1 (electronic), 978-1-6654-9771-8 (print)

Available from: 2022-06-03 Created: 2022-06-03 Last updated: 2023-11-08Bibliographically approved
Tripathy, A., van Deventer, J., Paniagua, C. & Delsing, J. (2022). OPC UA Service Discovery and Binding in a Service-Oriented Architecture. 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), University of Warwick, Coventry, United Kingdom, May 24-26, 2022. IEEE
Open this publication in new window or tab >>OPC UA Service Discovery and Binding in a Service-Oriented Architecture
2022 (English)In: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The OPC UA (Open Platform CommunicationsUnified Architecture) technology is found in many industrial applications as it addresses many of Industry 4.0’s requirements. One of its appeals is its service-oriented architecture. Nonetheless, it requires engineering efforts during deployment and maintenance o bind or associates the correct services to a client or consumer system. We propose the integration of OPC UA with the Eclipse Arrowhead Framework (EAF) to enable automatic service discovery and binding at runtime, reducing delays, costs, and errors. The integration also enables the client system to get the service endpoints by querying the service attributes or metadata. Moreover, this forms a bridge to other industrial communication technologies such as Modbus TCP (TransmissionControl Protocol) as the framework is not limited to a specific protocol. To demonstrate the idea, an indexed line with an industrial PLC (programmable logic controller) with an OPCUA server is used to show that the desired services endpoints are revealed at runtime when querying their descriptive attributes or metadata through the EAF’s Orchestrator system.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
OPC UA, Eclipse Arrowhead Framework (EAF), Automatic service discovery, Attribute-based service orchestration
National Category
Computer Sciences
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-90995 (URN)10.1109/ICPS51978.2022.9816880 (DOI)2-s2.0-85135623208 (Scopus ID)
Conference
5th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), University of Warwick, Coventry, United Kingdom, May 24-26, 2022
Funder
Vinnova
Note

Funder: EU ECSEL Joint Undertaking (826452);

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

Available from: 2022-06-03 Created: 2022-06-03 Last updated: 2023-11-08Bibliographically approved
Javed, S., Paniagua, C., Patil, S., van Deventer, J. & Delsing, J. (2022). Smart Adapter System Architecture for Seamless and Scalable Integration of Industry and Smart Home IoT. In: : . Paper presented at 48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022), Brussels, Belgium, October 17-20, 2022.
Open this publication in new window or tab >>Smart Adapter System Architecture for Seamless and Scalable Integration of Industry and Smart Home IoT
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2022 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-93731 (URN)
Conference
48th Annual Conference of the IEEE Industrial Electronics Society (IECON 2022), Brussels, Belgium, October 17-20, 2022
Projects
Arrowhead Tools
Funder
European Commission
Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2022-10-31Bibliographically approved
Javed, S., Paniagua, C., Patil, S., Van Deventer, J. & Delsing, J. (2022). Smart Adapter System Architecture for Seamless and Scalable Integration of Industry and Smart Home IoT. In: IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society: . Paper presented at IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, October 17-20, 2022. IEEE
Open this publication in new window or tab >>Smart Adapter System Architecture for Seamless and Scalable Integration of Industry and Smart Home IoT
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2022 (English)In: IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Integrating smart manufacturing ecosystems with industrial-grade smart energy and building automation systems enables real-time adaptation to changes in demands and factory conditions, the supply chain, and the needs of customers and society. However, integrating, managing, and controlling data exchange usually incurs high overheads in such a collaborative industrial environment. Smart home IoT technologies are a cost-effective solution for smart energy and building automation systems; they are not fully interoperable with industrial IoT technologies. This paper presents a mechanism to solve this interoperability problem using the Eclipse Arrowhead framework. The proposed solution provides a microservice-oriented architecture to develop protocol-specific smart adapter systems for the Arrowhead framework. These smart adapter systems provide seamless and highly scalable integrations between smart home and industrial IoT technologies. Our solution enables smart manufacturing ecosystems to meet Industry 5.0’s core values and reduce their carbon footprint to save the planet. We present the performance of our solution using an example from a real-world use case of a smart heating system scenario in a smart factory.

Place, publisher, year, edition, pages
IEEE, 2022
Series
Annual Conference of Industrial Electronics Society, ISSN 1553-572X, E-ISSN 2577-1647
Keywords
Eclipse Arrowhead Framework, Industrial Internet of Things, Industry 4.0, Industry 5.0, Interoperability, Smart Home Internet of Things, Z-Wave
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-95049 (URN)10.1109/IECON49645.2022.9969084 (DOI)2-s2.0-85143907126 (Scopus ID)978-1-6654-8025-3 (ISBN)
Conference
IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, October 17-20, 2022
Funder
European Commission, ECSEL JU, 826452
Available from: 2022-12-29 Created: 2022-12-29 Last updated: 2022-12-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3874-9968

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