The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024)
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 sig...
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MDPI AG
2025-06-01
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| author | Jacek Lukasz Wilk-Jakubowski Lukasz Pawlik Damian Frej Grzegorz Wilk-Jakubowski |
| author_facet | Jacek Lukasz Wilk-Jakubowski Lukasz Pawlik Damian Frej Grzegorz Wilk-Jakubowski |
| author_sort | Jacek Lukasz Wilk-Jakubowski |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7faf1730752a4aa7b4615fd7f240e07e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-7faf1730752a4aa7b4615fd7f240e07e2025-08-20T02:24:22ZengMDPI AGApplied Sciences2076-34172025-06-011512654910.3390/app15126549The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024)Jacek Lukasz Wilk-Jakubowski0Lukasz Pawlik1Damian Frej2Grzegorz Wilk-Jakubowski3Department of Information Systems, Kielce University of Technology, 7 Tysiąclecia Państwa Polskiego Ave., 25-314 Kielce, PolandDepartment of Information Systems, Kielce University of Technology, 7 Tysiąclecia Państwa Polskiego Ave., 25-314 Kielce, PolandDepartment of Automotive Engineering and Transport, Kielce University of Technology, 7 Tysiąclecia Państwa Polskiego Ave., 25-314 Kielce, PolandInstitute of Internal Security, Old Polish University of Applied Sciences, 49 Ponurego Piwnika Str., 25-666 Kielce, PolandThe 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.https://www.mdpi.com/2076-3417/15/12/6549vibration analysisacoustic signal processingmachine learningconvolutional neural networkshealth managementcondition monitoring |
| spellingShingle | Jacek Lukasz Wilk-Jakubowski Lukasz Pawlik Damian Frej Grzegorz Wilk-Jakubowski The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) Applied Sciences vibration analysis acoustic signal processing machine learning convolutional neural networks health management condition monitoring |
| title | The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) |
| title_full | The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) |
| title_fullStr | The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) |
| title_full_unstemmed | The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) |
| title_short | The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015–2024) |
| title_sort | evolution of machine learning in vibration and acoustics a decade of innovation 2015 2024 |
| topic | vibration analysis acoustic signal processing machine learning convolutional neural networks health management condition monitoring |
| url | https://www.mdpi.com/2076-3417/15/12/6549 |
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