## 3 Quantitative Analysis of Condition Monitoring and Predictive Maintenance
Other countries, such as South Korea (68), the UK (49), and Brazil (48), also demonstrate notable contributions, further emphasizing the international and collaborative nature of the research landscape. The colour intensity correlates with the publication frequency, and the numeric values for the top contributors are displayed on the map to provide additional clarity. This visualization underscores the geographic diversity and the varying levels of research activity across the globe. Figure 8 offers a comprehensive visualization of term dynamics over time in the realm of predictive maintenance and condition monitoring research. The chart displays critical terms on the vertical axis, representing key research topics, while the horizontal axis spans from 2019 to 2024. Each term is associated with a horizontal line, extending from its first quartile year (Year Q1) to its third quartile year (Year Q3), illustrating the temporal distribution of its relevance. The median year is marked with a blue circle, the size of which corresponds to the frequency of the term in the analysed literature. From the data, we observe that terms like \"condition monitoring\" and \"fault det  
Figure 7: Global distribution of research contributions, illustrating the frequency of publications by country in the field of predictive maintenance and condition monitoring. Labels highlight the top contributing countries, while the colour scale reflects the relative research intensity. frequencies (148 and 128, respectively), with a consistent presence in recent years (median in 2022). This underscores their critical importance in the field. Emerging terms such as \"deep learning\" and \"machine learning\" have median years of 2022 and 2023, respectively, highlighting their growing prominence as research priorities in recent years. Conversely, terms like \"signal processing\" and \"classification accuracy\" show relatively lower frequencies (22 and 10, respectively) and earlier median years (2020), suggesting a narrower or more specialized focus in earlier research efforts. Notably, terms such as \"prediction accuracy\" and \"data mining\" reflect the latest research developments, with all quartile years clustering around 2023 and 2024. This graph provides insights into how research priorities have shifted over time, illustrating the enduring relevance of foundational terms like \"neural networks\" and \"convolution\" while capturing the rise of emerging concepts like \"faults diagnosis\" and \"predictive maintenance\". These observations reveal the dynamic evolution of research in this field, highlighting key areas of focus and suggesting avenues for future exploration. In Figure 9, the analysis of connected terms within the field of predictive maintenance and condition monitoring is presented. The visualization groups related terms into distinct clusters, highlighting their interconnections and thematic focus areas. Green Cluster--Fault Diagnosis and Feature Extraction: This cluster emphasizes technical methodologies for identifying and diagnosing equipment faults. Terms such as _\"fault diagnosis\"_, _\"bearing\"_, _\"convolutional neural network\"_, and _\"signal\"_ are prominent, underscoring the importance of machine learning and signal processing in this area. Connections between _\"feature extraction\"_, _\"vibration signal\"_, and _\"neural network\"_ suggest a focus on leveraging advanced models to analyse and interpret diagnostic data. Red Cluster--Predictive Maintenance and Failure Prediction: Terms like _\"predictive maintenance\"_, _\"useful life\"_, _\"failure\"_, and _\"RUL prediction\"_ dominate this cluster, reflecting the industry's emphasis on forecasting equipment degradation and optimizing maintenance schedules. The strong interconnections highlight the integration of cost analysis, uncertainty management, and real-world applications in industrial settings. Figure 8: Term analysis over time, illustrating the temporal distribution of terms related to predictive maintenance and condition monitoring.