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Sensor Fusion and Data-Driven Techniques for Structural Health Monitoring
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Sensor Fusion and Data-Driven Techniques for Structural Health Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 1762

Special Issue Editor

Prof. Dr. Heung Soo Kim

E-Mail Website
Guest Editor
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul 780-714, Republic of Korea
Interests: smart structures; laminated composites
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advancements in sensor technology, combined with innovations in intelligent data-driven techniques, have revolutionized the field of structural health monitoring (SHM). This Special Issue aims to highlight active and passive sensing methods, sensor fusion, and intelligent data processing for the monitoring, assessment, and prediction of smart structures. The focus of this Special Issue will be on both hardware and software advancements in sensors and algorithms that improve data acquisition, signal processing, and real-time diagnostics.

The goal of this Special Issue is to provide a platform for interdisciplinary research that bridges the gap between sensor technologies and intelligent data-driven SHM systems, advancing the safety, reliability, and longevity of critical infrastructures across industries such as aerospace, automotives, and civil, and marine engineering.

Potential topics of interest include, but are not limited to:

  • Sensor fusion techniques for SHM in complex structures;
  • Active sensing methods for smart structures;
  • Passive sensing methods for smart structures;
  • Autonomous and real-time SHM using sensor networks;
  • Optimal sensor placement in smart structures;
  • Data augmentation techniques for SHM with limited sensors;
  • Advanced signal processing for noise-robust SHM;
  • SHM using wireless sensor networks and internet of things (IoT) integration;
  • Multi-sensor SHM systems;
  • Prognostics and health management (PHM) using sensor-driven SHM;
  • Machine learning and deep learning-based SHM.

Prof. Dr. Heung Soo Kim
Guest Editor

Mr. Muhammad Muzammil Azad
Guest Editor Assistant

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors 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 2600 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

  • smart structures
  • structural health monitoring
  • active sensing
  • passive sensing
  • sensor fusing
  • sensor network optimization
  • damage detection
  • localization

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Published Papers (1 paper)

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Research

17 pages, 4096 KiB  
Article
Damage Localization and Severity Assessment in Composite Structures Using Deep Learning Based on Lamb Waves
by Muhammad Muzammil Azad, Olivier Munyaneza, Jaehyun Jung, Jung Woo Sohn, Jang-Woo Han and Heung Soo Kim
Sensors 2024, 24(24), 8057; https://doi.org/10.3390/s24248057 - 17 Dec 2024
Cited by 2 | Viewed by 1330
Abstract
In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in [...] Read more.
In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems. Full article
(This article belongs to the Special Issue Sensor Fusion and Data-Driven Techniques for Structural Health Monitoring)
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