| DOI | Resolve DOI: https://doi.org/10.1117/12.2660270 |
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| Author | Search for: Liu, Zheng; Search for: Blasch, Erik; Search for: Liao, Min1; Search for: Yang, Chunsheng2; Search for: Tsukada, Kazuhiko; Search for: Meyendorf, Norbert |
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| Editor | Search for: Meyendorf, Norbert G.; Search for: Singh, Ripi; Search for: Niezrecki, Christopher |
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| Affiliation | - National Research Council of Canada. Aerospace
- National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, March 12-17, 2023, Long Beach, United States |
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| Subject | predictive maintenance; digital twin; information fusion; data modeling; analytic models; computing systems; analytics; computer simulations; data fusion; sensors |
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| Abstract | 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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| Publication date | 2023-04-25 |
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| Publisher | SPIE |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | ba32c5cb-6056-45f9-bd00-044b0a9483f9 |
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| Record created | 2024-07-11 |
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| Record modified | 2024-07-11 |
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