A Fog Computing Based Approach Towards Improving Asset Management and Performance of Wind Turbine Plants Using Digital Twins

Aliyu, Ibrahim; Perinpanayagam, Suresh; Hamidu, Ibrahim

doi:10.1109/icac55051.2022.9911170

Cited by 7 publications

(12 citation statements)

References 6 publications

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“…In their reference architecture, the authors outline how the edge-fog-cloud paradigm can allow a distributed DT to leverage the needed layers of computing and interfaces that are suitable for real-time applications. A similar concept was introduced in our earlier work [17].…”

Section: Digital Twins and Predictive Maintenancementioning

confidence: 96%

“…Existing DT architectures mostly adopt a centralized deployment strategy as, while some works show the importance of edge and fog computing in DT deployments. For instance, works like [17] [26] showed using edge or fog as an improvement in response time while [18] showed using edge computing for enhancement of data modelling for a machine tool DT. In this work we approach the distributed DT for the case study of predictive maintenance with a hybrid architecture utilizing both the edge-fog-cloud depending on the specific layer requirements.…”

Section: Digital Twin Architecturementioning

confidence: 99%

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Towards A Distributed Digital Twin Framework for Predictive Maintenance in Manufacturing Systems

Abdullahi,

Longo,

Samie

2024

Preprint

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This study uses a wind turbine case study to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real time condition monitoring, predictive analytics, and health management of selected components of Wind turbines in a wind farm.. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by Machine Learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as Condition Monitoring and Predictive Maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real time sensor data, and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

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Section: Digital Twins and Predictive Maintenancementioning

confidence: 96%

Section: Digital Twin Architecturementioning

confidence: 99%

Towards A Distributed Digital Twin Framework for Predictive Maintenance in Manufacturing Systems

Abdullahi,

Longo,

Samie

2024

Preprint

View full text Add to dashboard Cite

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Existing DT architectures mostly adopt a centralized deployment strategy, while some works show the importance of edge and fog computing in DT deployments. For instance, works like [ 19 , 30 ] showed the use of edge or fog as an improvement in response time, while [ 20 ] showed the use of edge computing for enhancement of data modeling for a machine tool DT. In this work, we approach the distributed DT for the case study of predictive maintenance with a hybrid architecture utilizing both the edge-fog-cloud depending on the specific layer requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

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Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Abdullahi,

Longo,

Samie

2024

Sensors

Self Cite

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This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

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Pivotal role of digital twins in the metaverse: A review

Sai,

Sharma,

Gaur

et al. 2024

Digital Communications and Networks

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A Fog Computing Based Approach Towards Improving Asset Management and Performance of Wind Turbine Plants Using Digital Twins

Cited by 7 publications

References 6 publications

Towards A Distributed Digital Twin Framework for Predictive Maintenance in Manufacturing Systems

Towards A Distributed Digital Twin Framework for Predictive Maintenance in Manufacturing Systems

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

Pivotal role of digital twins in the metaverse: A review

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