# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
###### Abstract
ML plays a key role throughout the analytical pipeline: from feature extraction (e.g., Principal Component Analysis (PCA) and Mel-Frequency Cepstral Coefficients (MFCCs)), through feature selection (e.g., Fisher Score), to classification or regression. In the context of sequential data, which is particularly important in time-series failure analysis, models such as long short-term memory (LSTM) and recurrent neural networks (RNNs) are used, as they can identify damage evolution patterns over time. Despite the achievements, many challenges remain to be addressed. The most important include the following [1; 10]:  
* Lack of standardized test datasets that would allow for the comparability of results;
* Difficulties in obtaining labeled data under real-world conditions, which is often costly and time-consuming;
* The need to develop models that are robust to noise and variability in environmental conditions. In response to these challenges, increasing attention is being directed toward unsupervised learning methods (such as autoencoders and clustering), transfer learning, and few-shot learning [10; 11]. The synergy between machine learning and vibration and acoustic analysis is gaining importance and becoming one of the most promising research areas in diagnostic engineering. Numerous studies confirm the effectiveness of machine learning methods in both laboratory and industrial applications from wind turbine monitoring and crack detection in composite materials to the diagnostics of railway components [12; 13]. Among these approaches, particular attention has been given to long short-term memory (LSTM) networks, a type of recurrent neural network designed to effectively model sequential data with long-range dependencies. Their ability to process noisy and complex time-series information makes them highly suitable for tasks such as structural health monitoring, where predicting stress and deformation based on limited sensor data is critical [14]In the following sections of this review, we will focus on the classification of available methods, case study analyses, and the identification of research gaps. It should be emphasized that vibrational phenomena are ubiquitous and accompany almost every aspect of human technical activity from vibrations in buildings and engineering infrastructure, through industrial machinery and vehicles, to such seemingly minor everyday elements as child car seats [15; 16]. This review seeks to address the following research questions:  
* Which families of machine learning algorithms currently dominate applications in vibration and acoustic signal analysis? * What performance metrics (e.g., accuracy, F1-score) are most commonly reported, and how have they evolved over the past decade? * Which diagnostic tasks--such as condition monitoring, health management, or damage detection--are most frequently targeted in these studies? * Have there been observable shifts in methodological approaches, including types of input data, evaluation strategies, or engineering domains, between 2015 and 2024? In each of these fields, vibration analysis serves not only as a diagnostic tool but also as a basis for evaluating comfort, safety, and operational reliability. In the case of large structures such as bridges or buildings, vibration measurements allow for the detection of structural damage and the assessment of its impact on stability. In transport and mechanical devices, they enable early fault detection, which can prevent failures. Even in such precise applications as child seat design, vibrations play a significant role influencing the comfort of the small passenger and safety during travel. The aim of this review article is to provide a comprehensive overview of the current state of knowledge regarding the application of machine learning methods to the analysis of vibration and acoustic signals in the context of technical diagnostics and the condition monitoring of structures and machines.