# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Country** & **2015–2019** & **2020–2024** & **All Years** & **Share [\%]** & **Chi-Square** \\ \hline All Countries & 23 & 73 & 96 & 100.0 & \(\chi^{2}\) \\ \hline India & 11 & 22 & 33 & 34.38 & \\ United States & 5 & 19 & 24 & 25.0 & \\ United Kingdom & 2 & 7 & 9 & 9.38 & \\ China & 1 & 7 & 8 & 8.33 & \\ Italy & 0 & 7 & 7 & 7.29 & \\ Japan & 0 & 6 & 6 & 6.25 & \(\chi^{2}=11.78\) \\ South Korea & 3 & 3 & 6 & 6.25 & (df = 11, \\ Iraq & 1 & 4 & 5 & 5.21 & \(p=0.3802\)) \\ Poland & 0 & 5 & 5 & 5.21 & \\ Brazil & 1 & 3 & 4 & 4.17 & \\ Germany & 0 & 3 & 3 & 3.12 & \\ Malaysia & 1 & 2 & 3 & 3.12 & \\ \hline \end{tabular}
\end{table} 
Table 3: Publications by year in different countries. no single nation experienced a dominant or disruptive rise in output during the second period. The growth appears to reflect a broad and balanced global engagement in ML-based vibration and acoustic diagnostics. It should also be noted that, in recent years, we are witnessing not only an increase in the number of publications but also a notable expansion of the research activity map. Machine learning in diagnostic applications is gaining importance globally--not only in traditionally strong centers but also in countries that are just beginning to build their position in this field. Figure 9, which is a mapping of connections between machine learning methods and main application areas, illustrates how specific ML techniques have been used in the context of particular diagnostic objectives. The intensity of the colors indicates the number of publications using a given method in a specific application area, enabling quick identification of dominant trends. The analysis clearly shows that the most frequently used method was SVM, which appeared in as many as 37 publications related to damage detection, 30 in condition monitoring, 23 in health management, and 15 in diagnostics. This broad range of use confirms the versatility of this technique and its effectiveness in vibroacoustic data analysis. Next in line were decision trees, which were particularly common in condition monitoring (25 publications), damage detection (24 publications), and diagnostics (17 publications). CNNs were also noticeably present, mainly in damage detection (16 publications) and diagnostics (six publications), as well as in other areas such as modeling and testing. NNSs appeared in 16 publications related to damage detection and 11 related to diagnostics, indicating their important role, especially in classification tasks based on time-frequency data. Methods such as autoencoders were used primarily in non-destructive testing (two publications), damage detection (four publications), diagnostics (two publications), and condition monitoring (three publications). RNNs and LSTM, as sequential models, were less frequent--appearing in two and three publications, respectively--which may suggest that their potential in vibroacoustic signal analysis is still being explored and developed. Similarly, clustering techniques such as K-means and ensemble methods like  
Figure 9: Map the connections between machine learning methods and application areas. RF had a limited scope--they were most often used in diagnostics, testing, and monitoring, but the number of instances was relatively low (e.g., RF appeared in only five studies). Thus, it should be noted that the mapping clearly indicates the dominant position of classical classification methods, such as SVM and decision trees, which appear in nearly all analyzed areas. On the other hand, deep learning methods and unsupervised clustering techniques, although currently applied more selectively, are gaining importance. It can be assumed that, as technology continues to advance and data access improves, their role will gradually increase, especially in more complex and dynamic diagnostic environments. Country-level trends presented in this review are based solely on the number of included publications affiliated with each country.