# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Technical Evaluation and Thoughtful Analysis  
The conducted studies show that diagnostic techniques based on ML have significantly improved in effectiveness compared to traditional methods. In many cases, very precise results were obtained, which were numerically confirmed across various industrial and engineering applications. For example, the use of ANNs and DTs enabled the classification of ball bearing faults with accuracies of 87% and 89%, respectively [16]. In the analysis of spectrogram images, convolutional neural networks achieved over 90% accuracy in identifying fan defects [6]. In [18], SCADA systems were combined with LDA and kNN methods, enabling effective assessment of cutting blade conditions--the classification accuracy exceeded 90%. In the context of larger structures such as suspension bridges, the use of ML--including clustering and kernel density estimation (KDE)--effectively supported vibration analysis, achieving over 85% accuracy [19]. Other studies showed that techniques such as K-means++ and k-NN are well-suited for detecting bushing faults at early stages of degradation, with accuracy exceeding 90% [20]. In the analysis of gear fatigue cracks, the J48 algorithm (a type of decision tree) achieved 93.3% accuracy, outperforming classical methods [2]. In water pipe diagnostics, the application of SVM also proved effective--with over 90% correct diagnoses, compared to only around 80% for simple threshold-based methods [21]. In the automotive sector, methods based on fuzzy logic and histogram analysis of vibration signals outperformed traditional statistical approaches, reaching approximately 85% accuracy [22]. Significant benefits were also observed with the use of CNNs in diagnostics of rotating machinery--the effectiveness of these methods was on average 20% higher compared to traditional feature extraction approaches [32]. Data fusion from multiple sensors further improved results by more than 15%, especially in the case of complex faults [5; 8; 91]. Automatic feature extraction using deep learning methods contributed to increasing the efficiency of the entire diagnostic process--by as much as 30% [7; 32]. Subsequent studies highlight the need to further improve precision through integration of data from different sources. This may allow for even more accurate detection of coexisting faults--up to 100% accuracy [9; 35]. Validation of these methods in real industrial conditions is also becoming increasingly important, particularly in terms of robustness to environmental noise, which can still reduce accuracy by 5-10% [38; 41; 45]. Unsupervised and semi-supervised learning methods are gaining importance, as they can reduce the need for labeled data by up to half while maintaining a high level of accuracy [1; 69]. The optimization of ML models for computational efficiency is also becoming a priority--especially in cases where simplicity and performance need to be combined. In such scenarios, classical algorithms like kNN and decision trees can achieve over 95% accuracy [39; 96]. More advanced approaches--such as autoencoders or GANs--can further improve model performance through the generation of synthetic data, resulting in accuracy gains of up to 10-20% [58; 75; 81]. Therefore, the topic of machine learning in the context of vibroacoustic signal analysis is gaining increasing attention. As system and data complexity grows, so does the demand for effective diagnostic methods capable of handling variable operating conditions, noise, and nonlinearity. Traditional approaches--such as FFT analysis or filtering--have limitations that modern ML techniques effectively overcome by automating anomaly detection, fault diagnosis, and failure prediction. Section 3 presents a quantitative analysis of scientific publications from 2015 to 2024 that met specific research criteria. This allowed for the identification of key development directions in the field of vibroacoustic diagnostics of machines and structures.