## 3 Quantitative Analysis of Condition Monitoring and Predictive Maintenance
The dataset encompasses a diverse range of document types, including 186 articles, 144 conference papers, and 12 reviews and smaller contributions such as book chapters (6), data papers (2), and short surveys (1). This visualization underscores the breadth and depth of the dataset, emphasizing its relevance to a comprehensive analysis of condition monitoring and predictive maintenance. The diverse range of contributions highlights the interdisciplinary and collaborative nature of research in this domain. The cumulative line chart in Figure 5 illustrates the top sources contributing to the bibliometric analysis of condition monitoring and predictive maintenance research over time. \"Sensors\" leads, with the highest cumulative occurrences, reflecting its significant role in the field, followed by \"IEEE Access\" and \"Measurement: Journal of the International Measurement Confederation\". Other notable contributions include \"IEEE Transactions on Instrumentation and Measurement\" and \"Mechanical Systems and Signal Processing\", which exhibit a steady increase in publications. Additional sources, such as \"Measurement Science and Technology\", \"Lecture Notes in Mechanical Engineering\", and \"Energies\", demonstrate the diversity of publication venues in this area. Furthermore, the inclusion of \"Lecture Notes in Networks and Systems\" underscores the interdisciplinary nature of the  
Figure 4: Overview of the bibliometric data analysed in this study, showcasing key metrics such as publication timespan (2017–2024); the number of sources, documents, and references; and collaboration statistics. research. This cumulative visualization highlights the progressive growth and collaborative efforts within the domain of condition monitoring and predictive maintenance. The analysis of key authors in the field of predictive maintenance and condition monitoring reveals significant contributions (see Figure 6). [PERSON], [PERSON], and [PERSON] stand out as leading contributors, consistently producing research that focuses on fault diagnosis and predictive methodologies using advanced machine learning techniques. Their work often intersects with applications in critical industrial equipment such as compressors, bearings, and conveyor systems, providing robust frameworks for predictive maintenance. Piccirilli MC demonstrates a specialized focus on the integration of signal processing techniques with deep learning for fault diagnosis, particularly in environments with noisy  
Figure 5: Visualization of top sources contributing to the dataset, highlighting key journals and conferences on condition monitoring and predictive maintenance. Figure 6: Author contributions and citations over time, illustrating the number of articles (bubble size) and yearly citations (colour scale) for each author in predictive maintenance and condition monitoring research. data. [PERSON] and [PERSON] emphasize the use of hybrid models, combining physical principles with data-driven approaches to improve the accuracy and interpretability of predictive systems. Their work addresses challenges such as concept drift and varying operating conditions in industrial processes. [PERSON] and [PERSON], though emerging contributors, showcase innovative applications of AI and IoT in asset management and real-time monitoring. Their efforts highlight the growing importance of interdisciplinary approaches in enhancing equipment reliability and operational efficiency. Collectively, these authors form a diverse and collaborative research landscape, driving advancements in predictive maintenance and its application across various industrial domains. In Figure 7, the global distribution of research contributions is visualized, showcasing the frequency of publications by country. The map highlights the significant role of China, with the highest frequency (439), followed by India (123), the USA (87), and Italy (80). These leading contributors are represented with darker shades, indicating their dominant position in research productivity.