# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 2 Materials and Methods
### Classification Criteria  
All classified publications were subjected to an in-depth content analysis related to all tasks assigned to the respective studies and methodological categories. The classification process was based on information available in publication metadata, such as:* Title, abstract, keywords, author attribution, and full text of the article when necessary. The first step was to determine the type of document, distinguishing between journal articles, conference papers, and other publications. * Book chapters, literature reviews, compiled letters, or research data. The second aspect of the classification involved identifying the country of affiliation of the authors. This allowed us to analyze the geographical distribution of the publications and assess research activity across different regions of the world. Another element was the identification of the machine learning algorithm used in each publication--these were assigned to one or more of the most commonly applied types: CNN, RNN, LSTM, autoencoders, SVM, DT, kNN, K-means clustering, and RFs. The general learning approach also considered the types of signals used, which were analyzed in time, frequency, time-frequency domains, or as multisensor data. For the purpose of this review, machine learning techniques were grouped into three general categories:  
* Classical methods, including support vector machines (SVMs), decision trees (DTs), k-nearest neighbors (kNNs), and random forests (RFs), which typically rely on manually engineered features and are known for their interpretability and computational efficiency. * Deep learning methods, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and autoencoders, which are capable of learning hierarchical representations directly from raw data, particularly effective for time-series and time-frequency signal analysis. * Hybrid approaches, which combine elements from both paradigms. For example, some studies use CNNs to automatically extract features from vibration or acoustic signals, and then apply classical classifiers like SVM or kNN for final classification. These methods aim to balance representational power and model simplicity. Key classification criteria also included the technical processes in which machine learning was applied. Based on content analysis, the activities presented in the publications were assigned to one or more of six application categories: condition monitoring (e.g., damage detection), diagnostics, health management, non-destructive testing, and modeling and design. In many cases, the publications reflected the multidisciplinary nature of the topics and diverse practical applications, often referring to multiple categories simultaneously. A similar situation applied to the classification of research methodologies, in which the type of conducted research was considered: experimental, case studies, conceptual work (including models and theories), and literature reviews. The relationships between individual algorithms and technical processes are presented in Figure 2.  
The above diagram visualizes the relationships between ML techniques, placed centrally, and their application contexts, arranged peripherally. The illustration shows typical algorithms such as CNN, SVM, RNN, and LSTM connected with symbolically represented processes such as diagnostics, monitoring, detection, or health management. This figure highlights not only the multidimensional nature of approaches used in the analysis of vibration and acoustic signals, but also their interconnections. For example, convolutional neural networks can be used both in non-destructive testing and in bearing diagnostics, whereas decision trees find application in condition monitoring classification systems or in predictive analysis. The graphical representation of these relationships helps to illustrate the complexity and flexibility of machine learning applications in the field of vibration and acoustic engineering.