# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Review from 2015 to 2024
In [33], a review of vibration-based damage identification studies in civil engineering structures using Deep Learning (DL) is presented. The paper discusses the effectiveness of DL tools, such as Convolutional Neural Networks (CNNs) and Unsupervised Pretrained Networks (UPNs), in detecting and localizing structural damage directly from raw sensor data. [PERSON] et al. [34] developed a portable vibration system for concrete diagnostics using SVM (70% accuracy). [PERSON] et al. [35] applied RF to evaluate CTWD distance in the WAAM process based on acoustic signals. Subsequent publications [6; 36] also addressed the use of CNNs, this time in the context of diagnostics of wind turbine gearboxes, transmission systems, and turning tools, highlighting the high effectiveness of this method. In [37], the authors focused on surface roughness prediction during steel milling based on acoustic emission. Both supervised and unsupervised ML algorithms were used, such as kNN, RBF-SVM, RF, K-means, and DBSCAN. The best results were obtained after feature reduction and cross-validation of the models. In article [38], the effectiveness of an acoustic camera and CNN in detecting gearbox anomalies was evaluated. Sound images were classified with 95% accuracy, even under noisy operating conditions. The method demonstrated potential as a non-destructive monitoring tool. In [39], the authors also examined the use of machine learning (SVM, Naive Bayes, kNN) for fault detection in wind turbine gearboxes based on vibration analysis. The best results were achieved with the Naive Bayes model (95.7%). This method improves turbine reliability and supports the development of renewable energy. Diagnostics of building structures, road and underwater infrastructure were widely discussed in works [1; 10; 26; 39; 40]. These studies used, among others, acoustic emission analysis, K-means, SVM, and FCBF (Fast Correlation-Based Filter), achieving accuracies close to 100%. Similarly effective were methods used for predicting the wear of cutting tools, valves, brakes, and transmission components, as confirmed by studies [3; 11; 27; 29; 30]. The topics of wind turbine diagnostics, FRP (Fiber-Reinforced Polymer) materials, marine environments, bearings, and asphalt surfaces were addressed in articles [38; 39; 40; 41; 42], where tools such as SVM, PCA, and Relieff (Relief-Based Feature Selection) were used, reaching accuracies up to 97%. Studies [43; 44; 45; 46; 47] focused on bearing vibrations, non-contact diagnostics, faults in electric motors, and turbines. Methods such as DAMM + SVM (Dual-Axis Motion Magnification +Support Vector Machine), laser vibration sensors, kNN, and XGBoost (Extreme Gradient Boosting) achieved very high accuracy--up to 99.4%. Equally promising results were obtained in the diagnostics of textile materials, BSP (Bearing Signal Processing), welding stress, and vehicle condition, as discussed in [48; 49; 50; 51; 52]. In studies [53; 54; 55; 12; 56], the use of advanced vibration and acoustic emission techniques in the diagnostics of steel structures, bridges, turbines, and composite materials CFRP (Carbon Fiber-Reinforced Polymer) was analyzed. Scalograms, FRF (Frequency Response Function), PCA, and classifiers such as kNN and SVM once again confirmed their effectiveness. The prediction of bridge stresses, diagnostics of ceramic composites, and data generation were the focus of research [57; 13; 58]. Further studies [59; 60; 61] addressed the impact of vibrations on operators, detection of road shocks, and structural monitoring. Diagnostics of brake systems and laser welds were presented in [62; 63; 64; 65]. Articles [66; 67; 68; 69; 70] discussed the diagnostics of machine components, while [71; 72; 73; 74; 75] focused on bolted joints, transformers, and acoustic waves. Studies [76; 77; 78; 79; 80] dealt with monitoring of cutting tools and autonomous underwater vehicles (AUVs).