## 3 Quantitative Analysis: Overview of Selected Studies  
This section provides an overview of the bibliometric analysis conducted for this study, highlighting key metrics, sources, author contributions, and prevalent research themes. Following the topic analysis using NLP, a selection of key topics was made, yielding an initial set of 283 articles. We subsequently refined these to 166 articles based on the methodology criteria. After a detailed review, 98 articles were retained for in-depth qualitative analysis; however, the general and quantitative analysis presented in this section is based on the broader dataset of 166 articles. These studies demonstrate significant advancements in predictive monitoring through the use of machine learning algorithms, with direct applications in fault detection and the optimization of maintenance for critical industrial equipment. The results showcase the diversity of publication sources, the collaborative nature of the research, and the prominence of advanced methodologies and key themes within the field. By mapping these factors, we outline the current state of research and identify specific opportunities for future investigations, such as how different publication types may influence the depth of empirical validation. Figure 3 illustrates the distribution of articles across different publication sources. The horizontal axis indicates the number of articles, while the vertical axis labels each source, providing a clear visual comparison. The data show that the majority of articles are concentrated in Lecture Notes in Networks and Systems with seven articles, followed by Applied Sciences, Energies, IEEE Access, and Mechanical Systems and Signal Processing, each contributing five articles. Other sources, such as Sensors and SDHM Structural Durability and Health Monitoring, have four and three articles, respectively. Specialized conference proceedings and technical publications, including ACM International Conference Proceeding Series, AISTECH - Iron and Steel Technology Proceedings, and American Society of Mechanical Engineers, PVP, contribute fewer articles, with only two each. This distribution highlights the predominance of articles in multidisciplinary journals and conference proceedings, suggesting a strong focus on sharing research in widely accessible platforms, while more specialized sources play a complementary role. Overall, these find  
ings reflect the breadth of publication venues, indicating that predictive maintenance in mining leverages a wide range of academic and industrial dissemination channels. Figure 4 provides an overview of the bibliometric data analyzed in this study. Here, the vertical axis lists the categories (for example, document types, authors, references), while the horizontal axis depicts their corresponding values. The dataset consists of 166 selected documents distributed across 123 unique sources. The results reveal that the data span 123 unique sources, comprising 166 documents with an average document age of 3.58 years and an annual growth rate of \(-\)2.21%. These documents collectively reference 4669 sources and include 1327 Keywords Plus and 472 Author's Keywords, highlighting the richness of the dataset. The collaboration metrics show that there are 541 authors, with 10 contributing to single-authored documents. Each document has an average of 3.53 co-authors, and international collaborations represent 13.25% of the total, indicating a global emphasis on predictive maintenance research. Regarding document types, the majority are articles (80), followed by conference papers (66), book chapters (3), and reviews (4), while books and retracted documents are minimal. By consolidating these diverse bibliometric indicators into a single figure, we offer a holistic view of collaboration patterns, publication formats, and keyword distributions, underscoring the interdisciplinary and applied nature of the field. Figure 5 presents a visualization of author contributions and citation performance over time.