Latest Developments Concerning Grand Challenges to Implement the Digital Twin Computing Concept

Nakamura, Takao; Mori, Koya; Kitahara, Ryo

doi:10.53829/ntr202304fa1

Cited by 1 publication

(3 citation statements)

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“…One direction to solve this issue is the "digital twin computing" (DTC) method. 44 If the DTC can handle the interactions between multiple digital twins, it becomes possible to create a system twin from asset twins and an asset twin from component twins.…”

Section: The Challenges and Opportunitiesmentioning

confidence: 99%

“…Digital twin computing (DTC) is an expansion of the conventional digital twin concept, which enables freely combination of various types of digital twins and diverse operations on them. 44 More importantly, it also enables mutual interactions in cyberspace beyond the reproduction of the physical twins. As shown on the right side of Fig.…”

Section: The Challenges and Opportunitiesmentioning

confidence: 99%

“…Figure 7: Digital twin computing architecture. 44 Figure 7 illustrates a five-layer architecture of digital twin computing, including the physical space layer, cyber/physical interaction layer, digital twin layer, digital world presentation layer, and application layer. The operations on digital twins, e.g., replication, fusion, and exchange, happen in the digital world presentation layer.…”

Section: The Challenges and Opportunitiesmentioning

confidence: 99%

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Digital twin for predictive maintenance

Liu¹,

Blasch

Liu

et al. 2023

NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE

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Digital twin engineering is a disruptive technology that creates a living data model of industrial assets. The living model will continually adapt to changes in the environment or operations using real-time sensory data as well as forecast the future of the corresponding infrastructure. A digital twin can be used to proactively identify potential issues with its real physical counterpart, allowing the prediction of the remaining useful life of the physical twin by leveraging a combination of physics-based models and data-driven analytics. The digital twin ecosystem comprises sensor and measurement technologies, industrial Internet of Things, simulation and modeling, and machine learning. This paper will review the digital twin technology and highlight its application in predictive maintenance applications.

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Section: The Challenges and Opportunitiesmentioning

confidence: 99%

Section: The Challenges and Opportunitiesmentioning

confidence: 99%

Section: The Challenges and Opportunitiesmentioning

confidence: 99%

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