# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 4 Results and Discussion  
Table 2 presents a review of scientific publications from the years 2015-2019 and 2020-2024 that focused on the application of ML methods in vibroacoustic signal analysis--particularly in the context of diagnostics of machinery, infrastructure, and industrial sys  
tems. In the first of these periods, 23 publications were identified, while in the subsequent five years the number rose to 73. In total, 96 studies were analyzed, over three-quarters (76%) of which were published in the latter period. This clearly demonstrates the growing interest in this subject area. In terms of document types, journal articles dominated--totaling 56 papers, which accounts for more than half of the entire dataset (58%). Conference papers appeared 38 times (39.6%), while only two publications (2%) were classified as \"other\". Statistical analysis indicates that the change in the distribution of document types over time is significant (\(\chi^{2}=7.02;p=0.029\)). This may suggest that conferences have gained importance in recent years as a platform for sharing research findings. A statistically significant relationship was found between the type of document and the time period. This indicates that the distribution of publication formats changed over time--in particular, conference papers became markedly more prevalent in the 2020-2024 period. This trend may reflect the growing pace of research dissemination and the expansion of ML topics in conference venues. Looking at specific machine learning methods, support vector machines were by far the most frequently used--appearing in 42 publications, which is nearly 44% of all cases. The next most common were general-purpose neural networks (NNs) and CNNs used in 18 and 17 studies, respectively. Notably, 16 out of the 17 CNN-related publications come from the 2020-2024 period, illustrating the sharp increase in interest in this technique. Overall analysis of ML methods confirmed significant shifts in their popularity over time (\(\chi^{2}=16.43\); \begin{table}
\begin{tabular}{c c c c c c} \hline
**Name** & **2015–2019** & **2020–2024** & **All Years** & **Share [\%]** & **Chi-Square** \\ \hline Total & 23 & 73 & 96 & 100.0 & \(\chi^{2}\) \\ \hline \multicolumn{6}{c}{Document Type} \\ \hline Conference Paper & 7 & 31 & 38 & 39.58 & \(\chi^{2}=7.021\) \\ Journal Article & 14 & 42 & 56 & 58.33 & (df = 2, \\ Other & 2 & 0 & 2 & 2.08 & \(p=0.029\)) \\ \hline \multicolumn{6}{c}{Machine Learning} \\ \hline CNN & 1 & 16 & 17 & 17.71 & \\ RNN & 0 & 2 & 2 & 2.08 & \\ LSTM & 0 & 3 & 3 & 3.12 & \\ Autoencoder & 1 & 3 & 4 & 4.17 & \(\chi^{2}=16.429\) \\ SVM & 12 & 30 & 42 & 43.75 & (df = 8, \\ Decision Tree & 11 & 17 & 28 & 29.17 & \(p=0.036\)) \\ NNS & 1 & 17 & 18 & 18.75 & \\ K-means & 0 & 6 & 6 & 6.25 & \\ Random Forest & 0 & 5 & 5 & 5.21 & \\ \hline \multicolumn{6}{c}{Process} \\ \hline Modeling and Design & 2 & 9 & 11 & 11.46 & \\ Health Management & 12 & 40 & 52 & 54.17 & \\ Condition Monitoring & 19 & 54 & 73 & 76.04 & \(\chi^{2}=0.478\) \\ Non-destructive Testing & 6 & 16 & 22 & 22.92 & (df = 5, \\ Damage Detection & 21 & 65 & 86 & 89.58 & \(p=0.992\)) \\ Diagnostics & 11 & 33 & 44 & 45.83 & \\ \hline \multicolumn{6}{c}{Research Methodology} \\ \hline Experiment & 14 & 39 & 53 & 55.21 & \\ Literature Analysis & 1 & 3 & 4 & 4.17 & \(\chi^{2}=2.458\) \\ Case Study & 8 & 16 & 24 & 25.0 & (df = 3, \\ Conceptual & 13 & 57 & 70 & 72.92 & \(p=0.482\)) \\ \hline \end{tabular}
\end{table} 
Table 2: Publications by year in all categories. \(p\) = 0.036), reflecting evolving technological trends in research. A significant shift was also observed in the types of machine learning methods employed. Specifically, deep learning models such as CNN and NNS became substantially more common in the recent period, replacing earlier dominance of classical techniques like SVMs and decision trees. This shift likely reflects broader adoption of neural architectures, availability of computing resources, and increasing dataset complexity.