Mobile Technologies in Predictive Maintenance for Industry 4.0: A Bibliometric Analysis of Research Trends, Knowledge Structure, and Future Directions

Abstract

The convergence of mobile technologies with predictive maintenance (PdM) systems represents a transformative paradigm in Industry 4.0 manufacturing environments, enabling real-time fault detection, remote diagnostics, and intelligent maintenance scheduling. This study presents a comprehensive bibliometric analysis mapping the intellectual structure, research trends, and knowledge evolution of mobile technologies in predictive maintenance applications from 2016 to 2025. Using systematic methodology adhering to PRISMA guidelines, 167 peer-reviewed publications were extracted from the Scopus database and analyzed using advanced bibliometric techniques, including citation analysis, co-authorship networks, keyword co-occurrence mapping, and thematic evolution assessment. The findings reveal exponential research growth from 1 publication in 2016 to 43 publications in 2024, representing a 3,900% increase and indicating rapid field maturation. India emerges as the dominant research contributor (49 publications, 29.3%), followed by the USA (18 publications, 10.8%) and Italy (15 publications, 9.0%), demonstrating significant geographic concentration in Asia- Pacific (39.5%) and European (22.4%) regions. Thematic analysis identifies augmented reality, the Internet of Things, and machine learning as core technological enablers, while co-citation analysis reveals Mourtzis D as the central intellectual hub connecting diverse research streams. The intellectual structure reveals four major research clusters: IoT-enabled mobile sensing, augmented reality applications, machine learning algorithms, and digital twin integration. Strategic thematic mapping positions predictive maintenance, augmented reality, and IoT as motor themes driving field advancement while identifying emerging opportunities in the industrial metaverse and 6G technologies. International collaboration analysis reveals hub-and-spoke patterns centred on India, with limited cross-cluster integration suggesting opportunities for enhanced global knowledge exchange. This bibliometric analysis provides the first comprehensive mapping of the mobile predictive maintenance (PdM) research landscape, offering evidence-based insights for researchers, practitioners, and policymakers navigating the evolving intersection of mobile technologies and smart manufacturing systems in Industry 4.0 contexts.