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Recent Developments in Acoustic Emission and Non-Destructive Evaluation

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 667

Special Issue Editors

School of Transportation, Southeast University, Nanjing 211189, China
Interests: acoustic emission technique; damage mechanics; multiscale modeling
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
Interests: hydraulic fracturing engineering; CCUS and microseismic and acoustic emission monitoring
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Special Issue Information

Dear Colleagues,

Acoustic emission (AE) has evolved into a versatile non-destructive evaluation (NDE) technique, now widely used in areas ranging from civil and mechanical structures to energy systems, advanced materials, and manufacturing processes for real-time damage detection, integrity assessment, and condition monitoring under service conditions. At the same time, the rapid development of digital twins, NDE 4.0, and artificial intelligence offers new opportunities to transform AE from a standalone diagnostic tool into a core data source for physics-informed and data-driven virtual representations of assets, enabling more intelligent, predictive, and life-cycle-oriented structural health management. We invite authorsto submit contributions to this Special Issue that both reflect the breadth of AE applications and demonstrate how AE can be integrated with emerging digital and AI technologies in non-destructive evaluation.

Potential topics include, but are not limited to, the following:

  • AE-based damage detection and characterization in civil, mechanical, aerospace, and energy structures;
  • AE applications in manufacturing and machinery;
  • Advanced AE sensing technologies and multi-component and hybrid NDE systems;
  • Digital twins and NDE 4.0 concepts incorporating AE data;
  • Mechanism- and data-driven methods for AE signal processing, source localization, pattern recognition, and prognosis.

Dr. Xing Cai
Dr. Shan Wu
Guest Editors

Manuscript Submission Information

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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

  • defect detection
  • damage monitoring
  • structural health monitoring
  • machinery condition monitoring
  • hybrid NDE systems
  • digital twins and NDE 4.0
  • mechanism- and data-driven AE
  • signal processing and source localization

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Published Papers (1 paper)

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Research

18 pages, 10815 KB  
Article
Synthetic Leak Data Generation Using Variational Autoencoders to Address Data Imbalance in Acoustic Emission-Based Pipe Leak Detection
by Byungjae Park, Hyejeong Ryu and Hyeongmin Yoo
Appl. Sci. 2026, 16(6), 3050; https://doi.org/10.3390/app16063050 - 21 Mar 2026
Viewed by 323
Abstract
A synthetic data generation method is proposed to mitigate data imbalance in pipeline leak detection using acoustic emission (AE) sensors. Collecting sufficient AE signals in the leak state is challenging due to the rarity of leaks and safety concerns. The rarity of leaks [...] Read more.
A synthetic data generation method is proposed to mitigate data imbalance in pipeline leak detection using acoustic emission (AE) sensors. Collecting sufficient AE signals in the leak state is challenging due to the rarity of leaks and safety concerns. The rarity of leaks leads to highly imbalanced datasets. The performance of leak detection methods may be degraded because the models tend to be biased towards the normal state. The proposed method utilizes a variational autoencoder (VAE) to probabilistically model the difference between the normal-state and leak-state spectrograms. After training the VAE with the spectrogram differences, the decoder of the VAE generates spectrogram differences from random latent vectors. Synthetic leak-state spectrograms are created by adding the generated spectrogram differences to normal-state spectrograms. The effectiveness of the proposed method is evaluated by comparing the leak detection performance of models trained with and without the proposed method. A leak detection model trained with synthetic leak data generated by the proposed method shows improved detection performance compared to models trained using existing oversampling methods. Full article
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