# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 6 Conclusions  
Between 2015 and 2024, research on the use of machine learning methods in vibroacoustic signal analysis has developed dynamically, particularly in the context of machine diagnostics, infrastructure, and industrial systems. A review of the literature covering 96 publications revealed a marked increase in research activity after 2020--with up to 76% of all studies published between 2020 and 2024. The greatest research interest was focused on processes such as damage detection (89.6% of publications), condition monitoring (76%), and diagnostics (45.8%). Among the machine learning methods used, classical approaches--mainly SVMs and DTs--were the most dominant, accounting for almost 40% of all applications. At the same time, there was a noticeable increase in the popularity of deep learning techniques, particularly CNNs and NNSs, which were most frequently applied in diagnostics, non-destructive testing, and condition monitoring. Less commonly used, but increasingly present, were methods such as autoencoders, RNNs, LSTM, and clustering techniques such as K-means and RFs. Their role was generally exploratory or complementary to the dominant approaches. From a methodological perspective, conceptual research clearly prevailed--appearing in over 70% of publications, which shows that the field still heavily relies on the development of theoretical foundations and models. However, there was also a visible increase in experimental studies (more than 30%), indicating a gradual transition from theory to practical implementation. Case studies were also regularly observed, especially in the context of non-destructive testing and modeling/design, serving as a bridge between theory and practice. Review articles in the literature represented only a small portion of the analyzed work, probably due to the novelty and fast-paced development of this area, where the focus remains on generating new solutions. Geographically, the highest research activity was recorded in India and the United States, which together represented a significant portion of all publications. The United States experienced a particularly intense growth--a 280% increase in publication output. China showed even greater dynamics--a 600% increase compared to the previous period. Interestingly, in recent years, new research centers have emerged on the scientific map, including Italy, Japan, Poland, and Germany, confirming the growing internationalization of research in this field. In summary, the literature review clearly indicates the growing importance of machine learning in technical diagnostics. The current focus is primarily on damage detection, monitoring, and system diagnostics. While classical methods such as SVMs and decision trees still form the backbone of many solutions, interest in deep learning techniques is increasing. Most works are conceptual in nature, but more and more are taking the form of experiments and case studies, indicating the field's maturation. In the coming years, further development should include not only the improvement and expansion of ML algorithms and their applications, but also efforts toward data standardization, the creation of open benchmarks, and the increased availability of real-world datasets. Key future directions will also include the exploration of transfer learning, unsupervised learning, and data fusion from various sensor sources, all of which can significantly enhance the effectiveness and reliability of modern diagnostic systems. In the next stages of this work, the authors plan to expand the scope of the analysis to include publications indexed in other reputable scientific databases, such as Web of Science, IEEE Xplore, SpringerLink, and ScienceDirect. This will allow for the current review to be supplemented with additional sources of knowledge and provide a more comprehensive picture of global trends in the application of machine learning to vibration and acoustic signal analysis.