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Applied Sciences
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Machine Learning in Vibration and Acoustics (3rd Edition)
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Machine Learning in Vibration and Acoustics (3rd Edition)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1064

Special Issue Editors

Dr. Chengjin Qin

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: PHM; machine learning; vibration and acoustics; signal processing; dynamics analysis and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liang Yu

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: machine learning; acoustic distributed and multisensor intelligent processing; vibration and acoustics

Special Issue Information

Dear Colleagues,

The reliability and quality requirements of modern industry equipment and products continue to increase. As we all know, vibration and sound contain rich information about the operation processes of equipment and products often used to monitor and analyze the state of systems. Over the past two decades, machine learning has been widely used in various fields of engineering due to its ability to learn complex problems. For this Special Issue, we are interested in articles on the latest research progress and achievements concerning machine learning in vibration and acoustics. Potential topics include, but are not limited to, the following:

  • Advanced vibration and sound data mining technology;
  • Advanced condition monitoring based on vibration and sound;
  • Advanced machine learning-based diagnosis and health assessment methods;
  • PHM based on vibration and acoustic information;
  • Acoustic distribution and multisensor intelligent processing;
  • Acoustic measurements and array signal processing;
  • Aeroacoustic signal processing;
  • Aeroengine acoustic testing and signal processing;
  • Aeroacoustic detection and security;
  • VR/AR/MR/CR technologies for the visual reconstruction and control of the sound field.

Dr. Chengjin Qin
Prof. Dr. Liang Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • vibration and acoustics
  • signal processing
  • PHM
  • acoustic measurements
  • condition monitoring
  • machine learning-based diagnosis and health assessment

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Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 3589 KB  
Article
Laplacian Manifold Learning Based Vibro-Acoustic Feature Fusion for Rail Corrugation Condition Characterization
by Yun Liao, Guifa Huang, Dawei Zhang, Xiaoqiong Zhan and Min Li
Appl. Sci. 2026, 16(1), 43; https://doi.org/10.3390/app16010043 (registering DOI) - 19 Dec 2025
Abstract
Accurate characterization of rail corrugation is essential for the operation and maintenance of urban rail transit. To enhance the representation capability for rail corrugation, this study proposes a sound–vibration feature fusion method based on Laplacian manifold learning. The method constructs a multidimensional feature [...] Read more.
Accurate characterization of rail corrugation is essential for the operation and maintenance of urban rail transit. To enhance the representation capability for rail corrugation, this study proposes a sound–vibration feature fusion method based on Laplacian manifold learning. The method constructs a multidimensional feature space using real-world acoustic and vibration signals measured from metro vehicles, introduces a Laplacian manifold structure to capture local geometric relationships among samples, and incorporates inter-class separability into traditional intra-class compactness metrics. Based on this, a comprehensive feature evaluation index Lr is developed to achieve adaptive feature ranking. The final fusion indicator, LWVAF, is generated through weighted feature integration and used for rail corrugation characterization. Validation on in-service metro line data demonstrates that, after rail grinding, LWVAF exhibits a more pronounced reduction and higher sensitivity to changes compared with individual acoustic or vibration features, reliably reflecting improvements in rail corrugation. The results confirm that the proposed method maintains strong robustness and physical interpretability even under small-sample and weak-label conditions, offering a new approach for sound–vibration fusion analysis and corrugation evolution studies. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics (3rd Edition))
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15 pages, 1840 KB  
Article
Accelerated Inverse Design of Multi-Parallel Microperforated Panel Absorbers via Physics-Informed Neural Networks
by Liyang Jiang, Bohan Cao, Ao Huang, Lei Yao and Jiangming Jin
Appl. Sci. 2025, 15(22), 11955; https://doi.org/10.3390/app152211955 - 11 Nov 2025
Viewed by 430
Abstract
Broadband sound absorption has long been a concern in noise control engineering, but the inverse design of multi-parallel microperforated panels (MPPs) for broadband sound absorption remains challenging. To address this issue, we propose a deep learning model that combines a variational autoencoder (VAE) [...] Read more.
Broadband sound absorption has long been a concern in noise control engineering, but the inverse design of multi-parallel microperforated panels (MPPs) for broadband sound absorption remains challenging. To address this issue, we propose a deep learning model that combines a variational autoencoder (VAE) with a physics-informed neural network (PINN) to accelerate the inverse design process of a multi-parallel MPP. Following Maa’s theory, we generated a dataset of 500,000 samples to train the model. By incorporating the PINN, we added an acoustic physical constraint to the loss function, promoting model convergence and the derivation of stable, unified parameters. The efficacy of the inverse design model was validated through theoretical analysis, finite element simulations, and impedance tube experiments. The experimental results show that the average sound absorption coefficient of multi-parallel MPPs within the frequency range of 500–1200 Hz is 0.85. Our work contributes to accelerating the inverse design of multi-parallel acoustic metamaterials. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics (3rd Edition))
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19 pages, 8605 KB  
Article
A Bayesian Grid-Free Framework with Global Optimization for Three-Dimensional Acoustic Source Imaging
by Daofang Feng, Kuncheng Wang, Youtai Shi, Liang Yu and Min Li
Appl. Sci. 2025, 15(20), 11028; https://doi.org/10.3390/app152011028 - 14 Oct 2025
Viewed by 401
Abstract
A common challenge in traditional three-dimensional grid-free localization is the struggle to balance computational efficiency with localization accuracy. To address this trade-off, a Bayesian grid-free framework with global optimization (BGG) for three-dimensional acoustic source imaging is proposed. In this method, a Bayesian inference [...] Read more.
A common challenge in traditional three-dimensional grid-free localization is the struggle to balance computational efficiency with localization accuracy. To address this trade-off, a Bayesian grid-free framework with global optimization (BGG) for three-dimensional acoustic source imaging is proposed. In this method, a Bayesian inference model is established based on equivalent source theory, where the negative log-posterior of the equivalent source positions serves as the fitness function. This function is minimized using a global optimization algorithm to estimate the source locations. Subsequently, the source strengths and noise variances are inferred via fixed-point iteration and projection-based estimation. Through both simulations and experiments with spatially distributed sources, a superior balance of computational efficiency and localization accuracy is demonstrated by the proposed BGG algorithm when compared to other state-of-the-art grid-free approaches. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics (3rd Edition))
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