# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 5 Further Development Prospects  
The analysis of the available literature clearly shows that the application of machine learning methods in vibroacoustic signal analysis is currently transitioning from conceptual developments to practical implementations in real industrial conditions. Supervised approaches, such as SVMs, decision trees, and classical neural networks, still dominate; however, solutions based on deep learning, unsupervised techniques, and hybrid configurations are increasingly emerging. In the coming years, further development of methods based on deep neural networks is expected, particularly CNN and 1D-CNN, which allow for the analysis of raw signals without the need for manual feature engineering. As a result, diagnostic systems become more reliable and faster in terms of data preparation, which is especially important in industrial environments [10, 11]. A key advantage of these methods is their ability to detect complex time-frequency patterns, making them highly effective in identifying subtle faults and anomalies in machine operation. Another promising development direction is data fusion from multiple measurement sources--such as vibration, acoustics, current, or temperature. Such approaches, especially when combined with feature selection techniques and ensemble learning, demonstrate high effectiveness [5, 10]. Integrating information from various signal types allows for a more comprehensive view of an object's technical condition, resulting in more accurate diagnostics and fewer false alarms. With the advancement of sensors and data acquisition systems, algorithms capable of integrating diverse data with low computational demand will become increasingly important. There is also a growing focus on prognostic strategies within the framework of condition-based maintenance (CBM). The key challenge here is not only detecting a fault but also estimating the time to failure and predicting the future progression of degradation. The literature indicates that the use of predictive algorithms in combination with acoustic signals can significantly improve maintenance scheduling and reduce operational costs [21]. An area that remains underexplored but highly promising involves unsupervised methods and novelty detection techniques. These allow for the identification of previously unknown anomalies without the need for large training datasets, which is particularly valuable in industrial practice where labeled data are often scarce [20, 42]. Such solutions have the potential to become the foundation for modern, autonomous diagnostic systems--especially in distributed and hard-to-monitor environments. In the context of industrial implementation, increasing importance is being placed on solutions aligned with the Industry 4.0 paradigm. The integration of diagnostic systems with the cloud, IoT platforms, SCADA, and edge computing technologies will be crucial for building modern, scalable monitoring infrastructures [3, 7]. This, in turn, drives the development of computationally lightweight, noise-robust models capable of operating in real time, even on resource-constrained devices. Attention should also be paid to the need for data standardization and the availability of open datasets. This would enable better model benchmarking, objective performance evaluation, and accelerated advancement of the entire field. At the same time, the importance of quality metrics is growing--not only in terms of classification accuracy, but also regarding model interpretability and robustness to data uncertainty. It is important to emphasize that the further development of machine learning in vibroacoustic signal analysis will aim toward greater system autonomy, better adaptability to changing conditions, integration with industrial digital ecosystems, and increased transparency of ML model behavior. All of this will help build user trust in algorithm-generated results and further unlock the practical potential of these technologies.