Digital twin for predictive maintenance

Zheng Liu; Erik Blasch; Min Liao; Chunsheng Yang; Kazuhiko Tsukada; Norbert Meyendorf

doi:10.1117/12.2660270

25 April 2023 Digital twin for predictive maintenance

Zheng Liu, Erik Blasch, Min Liao, Chunsheng Yang, Kazuhiko Tsukada, Norbert Meyendorf

Proceedings Volume 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE; 1248907 (2023) https://doi.org/10.1117/12.2660270
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

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.

Conference Presentation

Citation Download Citation

Zheng Liu, Erik Blasch, Min Liao, Chunsheng Yang, Kazuhiko Tsukada, and Norbert Meyendorf "Digital twin for predictive maintenance", Proc. SPIE 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, 1248907 (25 April 2023); https://doi.org/10.1117/12.2660270

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