# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 4 Results and Discussion
Citation counts or journal impact factors were not considered in this part of the analysis. Table 4 presents a summary of 96 scientific publications categorized by application areas, machine learning methods, and methodological approaches. It should be noted that a single publication could be assigned to multiple categories simultaneously; therefore, the total values do not sum to 100%. The analysis included six main thematic areas: modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. By far, the studies addressed damage detection the most (86 publications), clearly indicating that this topic attracts the greatest interest among researchers. Following that were condition monitoring (73 publications) and diagnostics (44 publications). The remaining areas--such as modeling (11 publications), health management (52 publications), and non-destructive testing (22 publications)--appeared less frequently, though they still formed an important part of the analyzed research. Regarding the machine learning techniques used, the most frequently applied method was SVM appearing in 42 publications. Interestingly, 37 of these were related to damage detection, 30 to non-destructive testing, and 23 to condition monitoring. Decision trees ranked second with 28 publications, most commonly in the context of diagnostics (17 publications) and non-destructive testing (25 publications). General-purpose neural networks (18 publications) and convolutional neural networks (17 publications) were also quite popular, with the vast majority (16 in each case) related to diagnostics. Less frequently appearing \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Modeling** & **Health** & **Condition** & **Non-Destructive** & **Damage** & **Diagnostics** & **Total** & **Chi-Square** \\  & **and Design** & **Management** & **Monitoring** & **Testing** & **Detection** & **Diagnostics** & **Total** & **Chi-Square** \\ \hline Total & 11 & 52 & 73 & 22 & 86 & 44 & 96 & \(\chi^{2}\) \\ \hline \hline \multicolumn{10}{c}{Machine Learning} \\ \hline CNN & 2 & 9 & 10 & 3 & 16 & 6 & 17 & \\ RNN & 0 & 2 & 1 & 1 & 1 & 0 & 2 & \\ LSTM & 0 & 3 & 2 & 1 & 2 & 0 & 3 & \\ Autoencoder & 0 & 3 & 3 & 2 & 4 & 2 & 4 & \(\chi^{2}=45.06\) \\ SVM & 5 & 23 & 30 & 12 & 37 & 15 & 42 & (df = 40, \\ Decision & 5 & 8 & 25 & 2 & 24 & 17 & 28 & \(p=0.268\)) \\ Tree & 1 & 9 & 14 & 4 & 16 & 11 & 18 & \\ NNS & 1 & 5 & 3 & 4 & 6 & 1 & 6 & \\ K-means & 1 & 3 & 2 & 3 & 0 & 2 & 0 & 5 & \\ Random & \multicolumn{10}{c}{Research Methodology} \\ \hline Experiment & 4 & 30 & 42 & 12 & 48 & 24 & 53 & \(\chi^{2}=5.52\) \\ Literature & 0 & 3 & 3 & 0 & 4 & 2 & 4 & (df = 15, \\ Analysis & 1 & 12 & 20 & 5 & 19 & 11 & 24 & \(p=0.987\)) \\ Case Study & 1 & 38 & 51 & 17 & 63 & 31 & 70 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Publications by process in other categories. techniques included RNN (two publications), LSTM (three publications), autoencoders (four publications), K-means (six publications), and RF (five publications). From a methodological perspective, conceptual publications made up the largest group--70 papers, reflecting the research community's strong focus on the development of new solutions, concepts, and models. Most of these concentrated on diagnostics (31 publications), testing (17 publications), and health management (38 publications). Experimental studies were the second-largest group (53 publications), followed by case studies (24 publications) and literature reviews (four publications). The statistical analysis did not reveal significant differences between ML methods and their application areas (\(\chi^{2}\) = 45.06; df = 40; \(p\) = 0.268), nor between methodological approaches (\(\chi^{2}\) = 5.52; df = 15; \(p\) = 0.987). A chi-square test was conducted to explore whether the choice of machine learning technique is associated with the specific application domain such as modeling, diagnostics, or damage detection.