# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 4 Results and Discussion
Regarding areas of application, the most frequently addressed topic was damage detection--present in as many as 86 publications (almost 90%). Condition monitoring appeared in 76% of the studies, and health management in just over half (54%). Less attention was given to system design (11%) and non-destructive testing methods (23%). Although the numerical data suggest a clear predominance of certain topics, the statistical test did not confirm significant temporal differences (\(\chi^{2}\) = 0.48; \(p\) = 0.992). No statistically significant association was detected between application domain and time period. This means that the focus areas--such as damage detection, condition monitoring, and health management--remained consistent across the decade, regardless of the ML methods used. This suggests a stable research interest in key diagnostic challenges. In terms of research methodology, conceptual studies were dominant--as many as 70 out of 96 publications (73%) were theoretical in nature. Experiments appeared in more than half of the cases (55%), and case studies were present in one in four publications. Literature reviews were relatively rare. In this case, as well, no significant changes were observed in the structure of research approaches between the two periods (\(\chi^{2}\) = 2.46; \(p\) = 0.482), which may suggest that the distribution of methods remains relatively stable despite the increase in the number of studies. There was no significant difference in the distribution of research methodologies over time. Experimental, conceptual, and case study-based approaches were used with similar frequency in both periods, indicating a sustained methodological balance in how research was conducted and reported. Figure 4 presents a comparison of the number of publications from the periods 2015-2019 and 2020-2024, broken down by document type. It is clearly visible that the number of publications increased significantly in all categories during the more recent period. The largest growth pertains to journal articles (\"Journal Article\"), which rose from 14 to 42. This increase may indicate a growing formalization and maturity of research in this field. A substantial rise was also observed in conference papers (\"Conference Paper\")--from 7 to 31. This may suggest increased activity within the scientific community and a growing number of research findings being presented at international forums. In contrast, publications classified as \"Other\" appeared only in the years 2015-2019 and were not present in the later period, which may indicate a shift away from less formal forms of presenting results. The overall trend suggests a dynamic development of the topic related to the application of machine learning methods in vibroacoustic signal analysis, particularly after 2020. The increased number of publications in peer-reviewed journals and at conferences confirms the growing interest of the research community and the maturation of this research domain. Figure 5 presents a comparison of the number of publications from 2015-2019 and 2020-2024, categorized by the machine learning methods used. The comparison clearly shows that, in recent years, the use of nearly all analyzed methods has increased significantly. SVM stands out in particular, having already been one of the most frequently used methods in the first period (12 publications), with the number increasing to 30 in 2020-2024. This confirms its established position in vibroacoustic diagnostics. Also noteworthy is the sharp rise in interest in deep learning methods. For CNNs, the number of publications rose from 1 to 16, while for NNSs it increased from 1 to 17. This surge indicates growing popularity of more advanced techniques, especially in the context of analyzing complex signals and diagnostic images. Decision trees also saw growth, increasing from 11 to 17 publications. Autoencoders appeared three times in the recent period, compared to just once between 2015 and 2019.