# 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  
The topic of ball bearing fault diagnosis using artificial neural networks and decision trees was discussed in detail in [17]. The authors demonstrated the effectiveness of these methods--87% for ANN and 89% for DT, respectively. Articles [7,8,18] presented the use of deep learning and techniques such as principal component analysis and mel-frequency cepstral coefficients in the analysis of spectrogram images and time-series data. These techniques achieved very high diagnostic accuracy, reaching up to 100%. Article [18] discussed the integration of accelerometers with SCADA systems and the use of classifiers such as linear discriminant analysis (LDA) and kNNs in industrial machine diagnostics, with particular emphasis on Industry 4.0 solutions. In the literature [2,5,19,20,21, 2], various machine learning algorithms appear--DTs, SVMs, RFs, and clustering methods--applied in the diagnostics of large structures, concrete prefabricates, gears, and misalignment in rotating machinery. The effectiveness of these approaches often exceeded 90%. In [22] compares three methods for assessing water pipe degradation: absolute threshold, linear discrimination, and SVM. The highest accuracy was achieved using SVM; however, the threshold method is less complex and effective when physical properties are known. In [23], a diagnostic method for hydraulic brake system faults was developed based on vibration signal analysis and machine learning. The combination of histogram features with a fuzzy classifier provided high classification performance. The proposed Adaptive Chirplet Transform (ACT) and the TFFNet network with an Embedded Feature Pyramid (EFP) in [24] enable effective classification of underwater acoustic signals. The method outperformed existing solutions on two real-world datasets. In [25], the authors evaluate the potential of using acoustic signals to monitor multiple machines. Time-frequency feature analysis allowed for the differentiation of machining parameters. The best results for predicting cutting depth were obtained using the Fine KNN classifier (62%). In [26], the effectiveness of 1D-CNN was evaluated in damage detection based on vibration, using both simulated and field data from a truss bridge. The model achieved an F1-score of 90.58%, outperforming other methods, even with single-channel measurements. In [27], the authors presented a method for evaluating tool wear based on acoustic emission signals during Al-SiC composite machining. The best results were obtained using a decision tree, achieving a blade condition prediction error below 6%. In [28], the authors presented a method for damage detection in planar frame joints using statistical features of the vibration signal and SVM. The algorithm effectively classifies the structural condition, confirming its usefulness in structural health monitoring. Article [29] introduced a bridge damage detection method using pre-trained CNNs (AlexNet, ResNet) and scalograms from wavelet transforms. An accuracy of 98-100% was achieved, preserving temporal and spatial information at a lower computational cost. In [30], an impact-based method combined with machine learning was used for non-destructive evaluation of Shinkansen track slabs. PCA and autoencoders enabled support condition differentiation with over 79% accuracy, supporting long-term railway infrastructure monitoring. In [31], the authors presented an early fault detection method for a reciprocating compressor based on vibration signal analysis. The highest effectiveness (99.67%) was achieved using the Lazy K Star classifier and autoregressive features. In [32], the authors showed that acoustic emission data can be used to accurately predict friction in steel-steel sliding contacts. The Gaussian process regression (GPR) model on acoustic emission histogram data achieved RMS 0.03 and r = 0.8, enabling remote friction monitoring under variable machine operating conditions. A review of DL methods confirmed the effectiveness of CNN and UPN in structural damage detection.