Digital twin meets information fusion: panel summary

Erik Blasch; Genshe Chen; Yu Chen; Yufeng Zheng; Lynne Grewe; Ivan Kadar; Andreas Savakis

doi:10.1117/12.3014996

7 June 2024 Digital twin meets information fusion: panel summary

Erik Blasch, Genshe Chen, Yu Chen, Yufeng Zheng, Lynne Grewe, Ivan Kadar, Andreas Savakis

Proceedings Volume 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 130570F (2024) https://doi.org/10.1117/12.3014996
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Within the recent years, the concept of Digital Twins (DT) emerged to support digital engineering physical-systems design in the processing and analysis of device components, data flows, and networked systems. As an element of systems engineering, the DT serves as a method for life cycle management for operations and maintenance through monitoring, diagnostics, and prognostics. Key to DT methods is the use of distributed sensors to monitor the system and determine the functioning with the use of physical design information, such as a series of distributed edge sensors and the layout of a physical electrical grid. While industries like industrial manufacturing, electrical power, space systems, and healthcare maintenance have embraced DT; other groups are utilizing the concept for analysis. Given that a large number of sensors are used to gather data on the health of system, it is natural that data fusion, estimation theory, and signals processing support digital twin fusion (DTwF); but there are a variety of challenges such as big data, distribution fusion, and edge analytics. The panel will discuss areas in which data and information fusion techniques enhance DT applications.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Erik Blasch, Genshe Chen, Yu Chen, Yufeng Zheng, Lynne Grewe, Ivan Kadar, and Andreas Savakis "Digital twin meets information fusion: panel summary", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 130570F (7 June 2024); https://doi.org/10.1117/12.3014996

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