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Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-2123-8187
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-9118-5861
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-3874-9968
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
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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 [en]
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: urn:nbn:se:ltu:diva-90195DOI: 10.1109/ICPS51978.2022.9816960Scopus ID: 2-s2.0-85135621043OAI: oai:DiVA.org:ltu-90195DiVA, id: diva2:1651859
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: 2024-12-09Bibliographically approved
In thesis
1. Towards Digitization and Machine learning Automation for Cyber-Physical System of Systems
Open this publication in new window or tab >>Towards Digitization and Machine learning Automation for Cyber-Physical System of Systems
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches.

In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model.

The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022. p. 47
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Digitization, Automation, Industry 4.0, Machine-to-Machine Translation, Ontology Alignment Eclipse Arrowhead Framework, Machine Learning, Unsupervised Learning, Condition Monitoring, Ontology Alignment
National Category
Computer Sciences
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-90196 (URN)978-91-8048-069-7 (ISBN)978-91-8048-070-3 (ISBN)
Presentation
2022-05-18, E632, Luleå tekniska universitet, Luleå, 08:30 (English)
Supervisors
Available from: 2022-04-13 Created: 2022-04-13 Last updated: 2023-09-05Bibliographically approved
2. Data-Driven Optimizations in Production Value Networks
Open this publication in new window or tab >>Data-Driven Optimizations in Production Value Networks
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industry 4.0 is transforming traditional production systems into dynamic and adaptive value networks. However, this shift reveals significant challenges in achieving seamless interoperability, workflow optimization, and effective value chain analysis within complex production environments. This thesis contributes to addressing these challenges through a structured approach, supporting data-driven and adaptive decision-making to optimize operations in dynamic production value networks.

The research commences by architecting microservice-oriented systems that facilitate the integration of legacy and brownfield technologies with Industry 4.0-compliant environments. By leveraging the Eclipse Arrowhead framework, the thesis demonstrates how diverse systems can exchange data and collaborate at runtime, establishing the foundation for cohesive and interoperable production networks.

Building on this interoperable structure, the thesis explores AI-driven optimizations across key areas, including workflow optimization, predictive maintenance, and demand response. These approaches support operational efficiency and adaptability in production value networks. Case studies showcase collaborative learning models for condition monitoring and an edge-based framework to optimize energy use, demonstrating tangible improvements in efficiency and resilience.

Finally, a significant contribution of this thesis is the introduction of tools for visualization and analysis of value chains. Using the Reference Architectural Model for Industry 4.0 (RAMI 4.0), the research provides methods to map and evaluate value creation within dynamic production networks. By integrating activity-based costing with microservice architectures, it offers granular insights into cost and value dynamics at runtime, enabling agile and informed decision-making in complex industrial environments.

Through these contributions, the thesis advances the understanding and implementation of data-driven optimizations in production value networks, supporting agility and sustainability while contributing to the transition to circular business models by enabling value chain analysis across the product lifecycle. The thesis serves as a bridge between theoretical frameworks and practical applications, providing valuable insights for both academia and industry, and paving the way for more efficient and sustainable production ecosystems.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2025
Keywords
Industry 4.0, Smart Industry Ecosystems, Microservice Architecture, Lifecycle Management, Run-time Value Chain Analysis, Real-time Decision Making, Production Value Networks, Stakeholder Collaboration
National Category
Engineering and Technology
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-111004 (URN)978-91-8048-716-0 (ISBN)978-91-8048-717-7 (ISBN)
Public defence
2025-02-26, A117, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Projects
Arrowhead fPVNAI REDGIO 5.0
Funder
European Commission
Available from: 2024-12-09 Created: 2024-12-09 Last updated: 2025-01-30Bibliographically approved

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Javed, SalehaJaved, Salmanvan Deventer, JanSandin, FredrikDelsing, JerkerLiwicki, MarcusMartin del Campo Barraza, Sergio

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