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Review
. 2024 Feb 12;24(4):1202.
doi: 10.3390/s24041202.

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Wei Li  1 , Haozhou Zhou  1 , Zhenyuan Lu  1 , Sagar Kamarthi  1
Affiliations
Review

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Wei Li et al. Sensors (Basel). .

Abstract

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

Keywords: artificial intelligence (AI); digital twins (DT); keyword co-occurrence network (KCN); scientometric analysis; sensors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Four levels of functionalities of digital twins.
Figure 2
Figure 2
Six primary digital twin application categories.
Figure 3
Figure 3
Number of articles, keywords, and links over the four time periods.
Figure 4
Figure 4
Growth trends in KCN parameters such as average network strength, maximum strength, average network degree, and maximum degree.
Figure 5
Figure 5
Boxplots of keyword degree, strength, and link weight distribution in the KCN.
Figure 6
Figure 6
Linecharts of KCN metrics: (a) probability density function of keyword degree, (b) average weight as a function of endpoint degree, (c) average weight as a function of endpoint degree, and (d) weighted clustering coefficient as a function of node degree.
Figure 6
Figure 6
Linecharts of KCN metrics: (a) probability density function of keyword degree, (b) average weight as a function of endpoint degree, (c) average weight as a function of endpoint degree, and (d) weighted clustering coefficient as a function of node degree.
Figure 7
Figure 7
Emerging and declining keywords of digital twin applications from 2000–2020 to 2023.
Figure 8
Figure 8
Emerging and declining keywords of digital twins sensing technology from 2000–2020 to 2023.
Figure 9
Figure 9
Mapping of sensor technology to digital twin application areas.
Figure 10
Figure 10
Mapping of machine learning methods to digital twins application fields.
Figure 11
Figure 11
Mapping of computation technology to digital twin application areas.
See this image and copyright information in PMC

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