# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 4 Results and Discussion
The result indicates no statistically significant relationship between method type and application area. This suggests that ML techniques are applied relatively uniformly across different diagnostic processes, and the selection of a specific algorithm (e.g., SVM, CNN, DT) does not appear to be strongly driven by the nature of the task. While some trends are observable such as the use of CNNs and NNSs in damage detection, these variations do not reach statistical significance. Another chi-square test evaluated whether research methodology (e.g., experimental, conceptual, case study) is associated with the type of diagnostic task addressed. The test yielded a very high \(p\)-value, clearly indicating no significant relationship. This means that researchers tend to adopt similar methodological approaches regardless of the domain whether working on modeling, condition monitoring, or diagnostics. The methodological style (e.g., conceptual vs. empirical) appears to be independent of the functional focus of the study. The second most frequently chosen methodological form was experimentation (53 publications), with the highest representation in research on condition monitoring (42 publications), damage detection (48 publications), and diagnostics (24 publications). Case studies appeared in 24 papers, most often in the context of health management (12 publications) and damage detection (19 publications). Conceptual works clearly dominated (70 publications), particularly in the areas of damage detection (63 publications), monitoring (51 publications), and health management (38 publications). Literature reviews were rare--observed in only four publications, mainly within the topic of diagnostics (two publications). Although the distribution of research methods across various areas indicates certain preferences, statistical analysis did not reveal significant dependencies. For machine learning methods, the chi-square test yielded a value of \(\chi^{2}\) = 45.06 (df = 40, \(p\) = 0.268), and for methodological approaches \(\chi^{2}\) = 5.52 (df = 15, \(p\) = 0.987). This means that no statistically significant relationship was found between the choice of method and a specific application area, although it is possible that hidden trends may emerge with larger datasets. Damage detection remains the most dominant thematic area (86 publications), and the most frequently used methods continue to be SVMs and decision trees. The predominance of conceptual and experimental studies confirms the dynamic development of the field--both in the creation of new models and theories, as well as in their practical implementation. Figure 10 presents a connection map illustrating the relationships between types of methodological approaches and main application areas. The color-coded intensity of occurrence makes it easier to quickly identify dominant links between methodology and research themes in the field of machine learning and vibroacoustic signal analysis. The strongest presence is observed in the case of conceptual studies, which appear in practically all areas and in very large numbers. They were most frequently used in publications concerning modeling and design (70 publications), diagnostics (63 publications), non-destructive testing (51 publications), and condition monitoring (38 publications). This indicates that the development of theoretical models and analytical frameworks forms the basis for further research activities in this domain. The concepts and models developed at this stage serve as the foundation for future experiments and practical implementations. Experimental studies ranked second, also present across many areas--albeit to a lesser extent. The highest number of such studies concerned modeling and design (53 publications), diagnostics (48 publications), non-destructive testing (42 publications), and condition monitoring (30 publications). This distribution suggests that theoretical concepts are gradually being translated into practical applications, tested in laboratory or industrial conditions.