# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
###### Abstract
Special attention will be paid to modern solutions developed over the past decade (2015-2024), including both classical methods such as support vector machines, k-nearest neighbors, and decision trees (DTs), as well as novel deep learning techniques, including convolutional neural networks, long short-term memory, and autoencoders. Due to the interdisciplinary nature of the subject, applications in various fields of engineering were also analyzed--from heavy industry and transportation infrastructure to energy and defense systems. Particular attention was paid to data integration from multiple sources (data fusion), automatic feature extraction, and unsupervised methods, as well as implementation challenges such as the lack of standardized datasets and the need to operate under varying environmental conditions and signal disturbances. As part of the review, 96 scientific publications indexed in the Scopus database between 2015 and 2024 were examined, with content containing key phrases related to \"vibration\", \"acoustics\", and \"machine learning\". During the analyzed period, a significant increase in the number of publications in this area was observed, clearly indicating growing interest in both the scientific community and industry regarding the use of artificial intelligence for monitoring and diagnostics. The conducted review also revealed research gaps and potential directions for future studies, including the development of noise-robust methods, few-shot learning, transfer learning, and the creation of open and diverse benchmark datasets. An important direction is also the full integration of machine learning methods with control, maintenance, and asset management systems, aligning with the concept of Industry 4.0. This review distinguishes itself from previous works by offering the first comprehensive, decade-spanning analysis (2015-2024) focused exclusively on the application of machine learning techniques in the field of vibration and acoustic signal analysis. Several prior review papers have addressed the role of machine learning in engineering diagnostics more broadly. For example, some studies have focused on specific algorithm classes (e.g., deep learning architectures), others on individual industries such as aerospace or automotive, or on narrow diagnostic modalities like bearing fault detection. However, these reviews often lacked a unified decade-spanning synthesis of vibration and acoustic signal applications across sectors. Moreover, few have systematically compared methodological approaches, geographical trends, and algorithm adoption patterns in this domain. This paper addresses that gap by providing a focused, decade-long analysis of 96 publications (2015-2024) that specifically apply machine learning to vibration and acoustic diagnostics across diverse engineering systems. While earlier reviews have either addressed broader diagnostic methods or focused on single aspects (e.g., specific algorithms or industrial sectors), this article presents an in-depth, multidimensional synthesis of 96 publications drawn from a targeted and methodologically rigorous Scopus-based selection. By categorizing research according to algorithm types, application domains (such as condition monitoring, damage detection, and non-destructive testing), and methodological approaches, this review not only maps the technological evolution in this field but also highlights research gaps and emerging trends. Furthermore, it includes a unique statistical analysis of temporal and geographical publication trends, providing valuable insights into the global landscape of machine learning-driven vibroacoustic diagnostics. This paper is organized as follows: Section 2 describes the document selection criteria and review methodology. Section 3 provides a thematic synthesis of the analyzed studies according to application domains and machine learning techniques. Section 4 presents a quantitative and bibliometric analysis. Section 5 outlines key challenges and future directions, and Section 6 concludes the review with final insights.