## 3 Quantitative Analysis of Condition Monitoring and Predictive Maintenance  
This section provides a bibliometric analysis centred on key topics identified through a systematic query designed to examine condition monitoring and predictive maintenance in industrial equipment. The selected topics include hydraulic pump condition monitoring and fault diagnosis, predictive maintenance for conveyor belt systems, remaining-useful-life estimation, bearing fault diagnosis using deep learning, and cable monitoring and predictive maintenance. These topics cover essential aspects of predictive maintenance, fo  
Figure 2: Flow chart depicting the NLP-based framework used to process abstracts collected from Scopus. cusing on challenges such as fault diagnosis accuracy, system reliability, and the application of advanced technologies like machine learning, deep learning, and signal processing. Each topic is analysed in detail, using bibliometric tools such as Bibliometrix to identify trends, key contributions, and research gaps. This approach offers valuable insights into the current state and future directions of predictive maintenance strategies for industrial systems. The dendrogram in Figure 3 illustrates the hierarchical clustering of topics generated from the bibliographic query, covering key areas in condition monitoring and predictive maintenance for industrial equipment. Six closely related topics are highlighted within the dendrogram, reflecting their thematic similarity: hydraulic pump condition monitoring and fault diagnosis, predictive maintenance for conveyor belt systems, remaining-useful-life estimation for predictive maintenance, bearing fault diagnosis using deep learning and signal processing, cable monitoring and predictive maintenance, and machine learning for fault diagnosis in air compressors. These topics represent critical areas of research with high interconnectivity, focusing on fault diagnosis accuracy, predictive capabilities, and system reliability. Other topics, including cardiac arrest outcomes prediction models, evaluation and analysis of AI chatbots in cancer care, Unmanned Aerial Vehicle (UAV) flight monitoring and failure prediction, and machine learning-based fault detection in DC-DC power converters, are more dispersed, indicating distinct research directions. This dendrogram provides a structured representation of thematic relationships, enabling a focused exploration of the most relevant topics in this study. It is important to clarify that the dendrogram reflects semantic similarity between documents--based on term co-occurrence and conceptual proximity--rather than strictly delineated application domains or fault types. As such, clusters may contain articles addressing multiple failure modes or technologies, requiring complementary expert interpretation for accurate topic synthesis. This rationale underpins the manual clustering and thematic validation strategy discussed later in the manuscript. Figure 4 provides an overview of the bibliometric data collected for this study. It highlights key metrics, including the timespan of publications (2017-2024) and the number  
Figure 3: Dendrogram illustrating the hierarchical clustering of topics, with the first six topics grouped closely together and highlighted for detailed analysis. of sources (242), documents (354), and references (10,556). The annual growth rate of publications is 31.36%, indicating a rapid increase in research activity within this period. The average document age is 3.29 years, and the average number of citations per document is 15.95, reflecting the relatively recent yet impactful nature of the research. In terms of authorship, there are 1166 contributing authors, with only 5 single-author documents, showcasing a high level of collaboration (an average of 4.07 co-authors per document). International co-authorships account for 19.77%, emphasizing the global scope of the research community.