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Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 5506

Special Issue Editors

Dr. Liangliang Cheng

E-Mail Website
Guest Editor
Group of Dynamics and Vibration, Engineering and Technology Institute Groningen, Faculty of Science and Engineering, University of Groningen, 9713 GZ Groningen, The Netherlands
Interests: rail vehicle vibration and dynamics; rail vehicle structural health monitoring and nondestructive testing; data-driven modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Yunpeng Zhu

E-Mail Website
Guest Editor
School of Engineering and Material Science, Queen Mary University of London, London E1 4NS, UK
Interests: data-driven dynamical systems; nonlinear dynamics; system identification; complex systems analysis and design; system condition monitoring; vibration isolation and control; energy harvesting; their engineering applications across a variety of disciplines
Dr. Saeid Hedayatrasa

E-Mail Website
Guest Editor
Mechanics of Materials and Structures (MMS), Department of Materials, Textiles and Chemical Engineering (MaTCH), Ghent University, Ghent, Belgium
Interests: non-destructive testing and structural health monitoring; vibrations and guided waves; infrared thermography; physics-informed machine learning; acoustic metamaterials

Special Issue Information

Dear Colleagues,

Nondestructive testing (NDT) and structural health monitoring (SHM) play a critical role in ensuring the safety and reliability of structures in a variety of industrial sectors, such as transportation systems (including automotive and aerospace), infrastructure, nuclear plants, and many others.

This has spurred the development and application of NDT and SHM technologies in numerous research studies and industrial communities around the world, with an emphasis on early defect/damage alerts through the use of novel sensing technologies, new measurement strategies, and data analysis algorithms.

This Special Issue aims to bring together excellent and talented researchers from around the world to showcase their advances in NDT and SHM methodologies for assessing the conditions of structures and systems in a wide range of engineering applications.

Topics of interest include, but are not limited to, the following:

  • Structural health monitoring;
  • System condition monitoring;
  • Nondestructive testing;
  • Dynamics and vibrations;
  • Vibration and control;
  • NDT sensors and techniques;
  • Damage indicator;
  • Sensor fusion;
  • Artificial intelligence;
  • Signal processing;
  • Damage detection and localization;
  • Uncertainty quantification;
  • Time series modelling;
  • Engineering application.

We are seeking high-quality contributions with original research results and review articles in the advances of NDT and SHM technologies in engineering applications.

Dr. Liangliang Cheng
Dr. Yunpeng Zhu
Dr. Saeid Hedayatrasa
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 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

  • structural health monitoring
  • system condition monitoring
  • nondestructive testing
  • dynamics and vibrations
  • vibration and control
  • NDT sensors and techniques
  • damage indicator
  • sensor fusion
  • artificial intelligence
  • signal processing
  • damage detection and localization
  • uncertainty quantification
  • time series modelling
  • engineering application

Published Papers (6 papers)

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Research

19 pages, 6714 KiB  
Article
Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network
by Zhunan Shen, Xiangwei Kong, Liu Cheng, Rengen Wang and Yunpeng Zhu
Sensors 2024, 24(4), 1290; https://doi.org/10.3390/s24041290 - 17 Feb 2024
Viewed by 586
Abstract
Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features [...] Read more.
Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features that are not controllable and have poor interpretability. To this end, this study proposes an innovative semi-supervised learning method for bearing fault diagnosis. Firstly, multi-scale dilated convolution squeeze-and-excitation residual blocks are designed to exact local and global features. Secondly, a classifier generative adversarial network is employed to achieve multi-task learning. Both unsupervised and supervised learning are performed simultaneously to improve the generalization ability. Finally, supervised learning is applied to fine-tune the final model, which can extract multi-scale features and be further improved by implicit data augmentation. Experiments on two datasets were carried out, and the results verified the superiority of the proposed method. Full article
(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)
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22 pages, 5486 KiB  
Article
An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction
by Fengxia He, Chuansheng Zheng, Chao Pang, Chengying Zhao, Mingyang Yang, Yunpeng Zhu, Zhong Luo, Haitao Luo, Lei Li and Haotian Jiang
Sensors 2024, 24(3), 951; https://doi.org/10.3390/s24030951 - 1 Feb 2024
Viewed by 632
Abstract
To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope [...] Read more.
To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis. Full article
(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)
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15 pages, 5738 KiB  
Article
An Investigation into the Application of Acceleration Responses’ Trendline for Bridge Damage Detection Using Quadratic Regression
by Hadi Kordestani, Chunwei Zhang and Ali Arab
Sensors 2024, 24(2), 410; https://doi.org/10.3390/s24020410 - 9 Jan 2024
Viewed by 811
Abstract
It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply [...] Read more.
It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method. Full article
(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)
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23 pages, 6849 KiB  
Article
A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images
by Jianan Cao, Yue Gao and Chuanyang Wang
Sensors 2023, 23(22), 9030; https://doi.org/10.3390/s23229030 - 7 Nov 2023
Viewed by 893
Abstract
Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, [...] Read more.
Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform. Full article
(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)
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14 pages, 3395 KiB  
Article
Quantification of Construction Materials Quality via Frequency Response Measurements: A Mobile Testing Station
by Lukasz Scislo and Nina Szczepanik-Scislo
Sensors 2023, 23(21), 8884; https://doi.org/10.3390/s23218884 - 1 Nov 2023
Cited by 1 | Viewed by 693
Abstract
In construction, ensuring the quality and compliance of materials with specified requirements is often challenging, especially at construction sites. Conventionally, this process necessitates transporting samples to well-equipped laboratories, incurring significant time and financial costs. This article proposes a novel approach through a cost-effective [...] Read more.
In construction, ensuring the quality and compliance of materials with specified requirements is often challenging, especially at construction sites. Conventionally, this process necessitates transporting samples to well-equipped laboratories, incurring significant time and financial costs. This article proposes a novel approach through a cost-effective mobile test station, enabling on-site measurements and immediate evaluation results, regardless of the testing conditions. The foundation of our testing methodology lies in the Impulse Excitation Technique (IET), which capitalises on measuring the frequency response of samples while considering their mass and dimensions. By applying this technique, we can effectively determine crucial elastic properties, such as the Young Modulus and Poisson Ratio. These obtained values can then be cross-referenced with established material tables to verify the material’s compliance with the specified order. In this study, the developed universal and mobile test station demonstrated versatility by successfully evaluating three samples of typical construction materials, showing the method’s reliability on some real case measurements. The results substantiate its potential as a reliable mobile quality assurance station. Moreover, the station’s adaptability empowers its use on site, in laboratory settings, or even during material transportation when necessary. This innovation promises to revolutionise material quality assessment, streamlining the construction process and expediting decision making. Full article
(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)
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14 pages, 5286 KiB  
Article
Detection and Identification for Void of Concrete Structure by Air-Coupled Impact-Echo Method
by Jinghui Ju, Xiushu Tian, Weigang Zhao and Yong Yang
Sensors 2023, 23(13), 6018; https://doi.org/10.3390/s23136018 - 29 Jun 2023
Cited by 1 | Viewed by 1339
Abstract
In the field of non-destructive testing (NDT) for concrete structures, the traditional air-coupled impact-echo technology often has the problems of complex operation and low efficiency. In order to solve these problems, this study uses Comsol software to establish a finite element model (FEM) [...] Read more.
In the field of non-destructive testing (NDT) for concrete structures, the traditional air-coupled impact-echo technology often has the problems of complex operation and low efficiency. In order to solve these problems, this study uses Comsol software to establish a finite element model (FEM) of the concrete structure with different void sizes and obtains the variation rule of peak frequency. The recognition property of the concrete void based on peak frequency is proposed, which is explained and validated by relevant theory and experiments. The results show that compared with the depth of the void, the influence of the void width on the peak frequency increases significantly. When the void width is greater than 0.3 m, the peak frequency of the sound wave decreases with the increase in the width, and the change is obvious. This paper describes the applicability of concrete void depth less than 0.4 m for the air-coupled method and, when the concrete void depth is less than 0.4 m, the peak frequency can be used to effectively identify void widths greater than 0.3 m. The research results will be beneficial to void detection of concrete structures such as tunnel lining and pavements. Full article
(This article belongs to the Special Issue Application of Dynamical Analysis and Sensing Technologies in Nondestructive Testing and Structural Health Monitoring)
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