# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
###### Abstract  
The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field's evolution and identifying its key research challenges and innovation trajectories. v 2025 15 6549 10 June 2025 10  
signal nonlinearity, and the presence of noise. In such cases, ML becomes a key tool, enabling the automation of pattern detection, damage classification, and degradation prediction, even when dealing with complex and multidimensional data [2; 3]. Vibrational and acoustic signals have long been the foundation of machine and structural diagnostics--covering a wide range of elements, from bearings and gearboxes to bridges and concrete structures [4]. In the past, methods such as Fast Fourier Transform (FFT), band-pass filtering, envelope analysis, and wavelet transform were applied. Today, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), autoencoders, and classical ML algorithms such as support vector machines (SVMs), k-nearest neighbors (kNNs), random forest (RF), and K-means clustering come to the forefront [5; 6]. An example of machine learning application can be found in a study on the diagnostics of Electrical Submersible Pumps (ESPs), where raw time-domain data from vibration sensors were used as input for a convolutional neural network, eliminating the need for manual feature extraction. Another study classified vibration signal spectra in the form of images, which were then processed using support vector machines (SVMs), artificial neural networks (ANNs), and the k-nearest neighbors (kNNs) algorithm, achieving high diagnostic accuracy for fans [7]. [PERSON] and his team [8] successfully applied a convolutional neural network (CNN) architecture to classify acoustic signals from road surfaces (dry and wet), obtaining an accuracy of 96.36%. Significant progress has also been observed in data fusion methods and multisensor systems [9]. Increasingly, vibration signals are analyzed alongside current, acoustic, or optical signals, enhancing the reliability of machine learning classifiers. Such approaches are gaining importance in the context of Industry 4.0, where data from Supervisory Control and Data Acquisition (SCADA) systems can be enriched with real-time physical measurements [5; 9].