A BIBLIOMETRIC REVIEW OF RESEARCH PUBLICATIONS ON DIGITAL TWIN PREDICTIVE MAINTENANCE SYSTEMS IN THE MARITIME INDUSTRY

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Keywords

Digital Twin (DT); Predictive maintenance system; Maritime industry; Industry 4.0; Bibliometric; Digital Twin Predictive Maintenance System (DTPMS); Artificial intelligence (AI); Internet of Things (IoT); Total publications (TP); Total citations (TC)

Degree Level

Advisor

Degree Name

Volume

19

Issue

2

Publisher

Memorial University of Newfoundland, Fisheries and Marine Institute, Centre for Applied Ocean Technology

Abstract

This bibliometric review delves into the topic of “Digital Twin Predictive Maintenance System in the Maritime Industry,” examining existing research to identify trends and potential avenues for future exploration. Through analysis of 12 data clusters (consisting of 1,074 publications) from maritime sources, this study uncovers significant growth in interest from 2016 onwards and synthesizes key findings from the historical evolution of Digital Twins. The review highlights various research clusters, including advancements in Digital Twin technology, Smart Manufacturing applications, and the integration of Blockchain. By using bibliometric techniques, the study maps country collaborations and illustrates international research networks in this field. It also highlights the most cited papers, underlining influential contributions and their impact. This comprehensive review offers a unique perspective on the development, collaborations, and key research themes in the context of Digital Twin Predictive Maintenance Systems within the maritime industry.