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Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis.
Wei Li1, Haozhou Zhou1, Zhenyuan Lu1
1Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA.
Sensors (Basel, Switzerland)
|February 24, 2024
Summary
Digital twin research is rapidly diversifying, focusing on predictive functions and real-time data integration. This evolution highlights advanced sensing technologies and distributed computation for enhanced system modeling.
Area of Science:
- Computer Science
- Engineering
- Information Science
Background:
- Digital twin technology is revolutionizing data integration and system modeling across manufacturing, energy, and healthcare.
- The technology enables complex system representation and analysis.
Purpose of the Study:
- To explore the evolving research landscape of digital twins.
- To analyze trends, keyword interconnections, and application areas using Keyword Co-occurrence Network (KCN) analysis.
Main Methods:
- Analysis of metadata from 9639 peer-reviewed articles (2000-2023).
- Keyword Co-occurrence Network (KCN) analysis to map trends and interconnections.
- Mapping of sensing technology keywords to six application areas.
Main Results:
- Digital twin research shows rapid diversification with emerging themes like predictive and decision-making functions.
- Emphasis on real-time data, point cloud technologies, federated learning, and edge computing for distributed computation and data privacy.
- Identification of focused themes and the integration of advanced sensing technologies.
Conclusions:
- Digital twins have evolved into complex systems capable of predictive operations via advanced sensing.
- Challenges remain in sensor selection and empirical knowledge integration.
- The study confirms the growing importance and diversification of digital twin research.
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