## 3 Quantitative Analysis: Overview of Selected Studies
The scatter plot highlights variations in the number of articles published by different authors between 2015 and 2025, as well as the annual citation impact of their work. Larger bubbles represent authors with a higher number of publications, while the color gradient indicates citations per year, ranging from low (purple) to high (yellow). Figure 3: Number of articles per source. The figure shows the distribution of articles across various publication sources, emphasizing the dominance of certain journals and proceedings in the field. Authors such as [PERSON] demonstrate consistent contributions over multiple years with a strong citation performance, as indicated by the prominent yellow bubble in 2015. Other authors, such as [PERSON] and [PERSON], have fewer publications but maintain a moderate citation rate, emphasizing their targeted yet impactful contributions. The overall trend suggests that authors with diverse publication years tend to accumulate higher annual citations, underlining the importance of sustained research efforts and consistent dissemination of findings. This analysis provides valuable insights into the dynamics of academic contributions and citation impact, offering a foundation for understanding collaborative patterns and research trends over time. Figure 4: Overview of bibliometric data. The figure illustrates key metrics, including the number of sources, documents, collaboration statistics, and document types, providing a comprehensive view of the analyzed dataset. Figure 5: Author contributions and citation performance over time. The size of each bubble represents the number of articles, while the color gradient reflects citations per year, with yellow indicating the highest impact. Figure 6 highlights the frequency of key terms found in the abstracts of the analyzed documents. \"Condition Monitoring\" appears as the most frequently used term with 74 occurrences, followed by \"Data Mining\" and \"Predictive Maintenance\", each with 46 mentions. Other terms such as \"Machine Learning\" (41 occurrences) and \"Fault Detection\" (34 occurrences) also feature prominently, reflecting the emphasis on advanced technologies in monitoring and maintenance. Notably, terms like \"Decision Trees\" and \"Feature Extraction\", with 19 and 20 mentions, respectively, suggest a focus on specific methodologies within the broader research landscape. This analysis provides insights into the dominant themes and techniques prevalent in the field. Beyond these explicitly identified terms, additional themes have emerged, indicating an expanding research landscape that incorporates advanced mathematics, nonlinear analysis, and optimization-based programming. Topics such as \"chaotic approaches\" [36; 37], \"complex fluid systems\" [38; 39], and \"multi-stage optimization\" [40; 41] have been identified, suggesting the integration of interdisciplinary methodologies. Furthermore, the presence of more specialized keywords, such as \"aviation engine monitoring\" [42; 43] and \"intelligent instrumentation\" [44; 45], highlights the cross-pollination of methodologies originally developed in aerospace and energy sectors, now being adapted to mining applications [46; 47; 48; 49; 50]. This thematic expansion underscores a broader trend: mining operations are increasingly moving beyond traditional preventive programs and linear models. Instead, more sophisticated paradigms are being adopted, involving high-dimensional sensor data, real-time analytics, and advanced modeling techniques capable of handling large parameter spaces. Chaotic approaches, for instance, offer new avenues for modeling and predicting complex and nonlinear behaviors frequently observed in mining environments, such as conveyor systems under variable loads [36; 37]. Similarly, fluid-driven processes in tailing ponds or slurry pipelines are subject to multiphysical phenomena, making \"complex fluid systems\" an emerging area of interest [38; 39].