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Recent Advances in Nondestructive Testing and Structural Health Monitoring
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Recent Advances in Nondestructive Testing and Structural Health Monitoring

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5527

Special Issue Editors

Dr. Hongchen Miao

E-Mail Website
Guest Editor
School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: ultrasonic guided-wave transducer development and testing; SH wave metasurface; spectral methods for modelling of wave propagation in structures
Dr. Yanfeng Shen

E-Mail Website
Guest Editor
University of Michigan–Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: structural health monitoring; nondestructrive evaluation; guided waves; ultrasonics; sensors and actuators
Special Issues, Collections and Topics in MDPI journals
Dr. Cuixiang Pei

E-Mail Website
Guest Editor
School of Aerospace, Xi’an Jiaotong University, Xi'an 710049, China
Interests: laser ultrasonics; nondestructive evaluation; electromagnetic acoustic transducers; guided wave; infrared thermography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past several decades, we have seen the rapid development of nondestructive testing (NDT) and structural health monitoring (SHM) techniques as well as their great success in the inspection of various materials and structures. Advanced sensor systems have taken NDT and SHM discipline to a new era. However, emerging challenges and demands from industries are constantly imposing growing requirements for the development of sensing technologies. This Special Issue is therefore dedicated to the recent advances in research and progress of inspection sensors and systems, as well as on novel and innovative applications of established techniques in the fields of NDT and SHM. Topics of interest include, but are not limited to:

  1. Ultrasonic bulk- or guided-wave piezoelectric transducers;
  2. Electromagnetic acoustic transducers (both bulk wave and guided wave);
  3. Eddy current sensors;
  4. Electromagnetic sensors;
  5. Laser ultrasonics;
  6. Optical sensors;
  7. Infrared thermography sensors;
  8. Fiber-optical sensors and networks;
  9. Sensing algorithms;
  10. AI for NDE and SHM.

Dr. Hongchen Miao
Dr. Yanfeng Shen
Dr. Cuixiang Pei
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.

Published Papers (4 papers)

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Research

16 pages, 2882 KiB  
Article
WD-YOLO: A More Accurate YOLO for Defect Detection in Weld X-ray Images
by Kailai Pan, Haiyang Hu and Pan Gu
Sensors 2023, 23(21), 8677; https://doi.org/10.3390/s23218677 - 24 Oct 2023
Cited by 1 | Viewed by 1305
Abstract
X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we [...] Read more.
X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second. Full article
(This article belongs to the Special Issue Recent Advances in Nondestructive Testing and Structural Health Monitoring)
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15 pages, 8771 KiB  
Article
Studies of Angular Resolution for Acoustic Arc Arrays
by Dmitry A. Sednev, Alexey I. Soldatov, Andrey A. Soldatov, Maria A. Kostina, Dmitry O. Dolmatov and Daria A. Koneva
Sensors 2023, 23(13), 6007; https://doi.org/10.3390/s23136007 - 28 Jun 2023
Viewed by 834
Abstract
Currently, phased arrays are increasingly used in ultrasonic nondestructive testing. One of the most important parameters of ultrasonic nondestructive testing with the application of phased arrays is the angular resolution. This paper presents the results of studies of the angular resolution of concave [...] Read more.
Currently, phased arrays are increasingly used in ultrasonic nondestructive testing. One of the most important parameters of ultrasonic nondestructive testing with the application of phased arrays is the angular resolution. This paper presents the results of studies of the angular resolution of concave and convex acoustic arrays in ultrasonic testing with the application of the total focusing method. Computer modeling of concave and convex acoustic arrays consisting of 16, 32 and 64 elements with distances between elements of 0.5 and 1 mm and arc radii of 30 and 60 mm have been performed. The results obtained by computer modeling were confirmed via in situ experiments. Full article
(This article belongs to the Special Issue Recent Advances in Nondestructive Testing and Structural Health Monitoring)
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21 pages, 5895 KiB  
Article
Detection of Missing Bolts for Engineering Structures in Natural Environment Using Machine Vision and Deep Learning
by Zhenglin Yang, Yadian Zhao and Chao Xu
Sensors 2023, 23(12), 5655; https://doi.org/10.3390/s23125655 - 16 Jun 2023
Cited by 1 | Viewed by 1408
Abstract
The development of an accurate and efficient method for detecting missing bolts in engineering structures is crucial. To this end, a missing bolt detection method that leveraged machine vision and deep learning was developed. First, a comprehensive dataset of bolt images captured under [...] Read more.
The development of an accurate and efficient method for detecting missing bolts in engineering structures is crucial. To this end, a missing bolt detection method that leveraged machine vision and deep learning was developed. First, a comprehensive dataset of bolt images captured under natural conditions was constructed, which improved the generality and recognition accuracy of the trained bolt target detection model. Second, three deep learning network models, namely, YOLOv4, YOLOv5s, and YOLOXs, were compared, and YOLOv5s was selected as the bolt target detection model. With YOLOv5s as the target recognition model, the bolt head and bolt nut had average precisions of 0.93 and 0.903, respectively. Third, a missing bolt detection method based on perspective transformation and IoU was presented and validated under laboratory conditions. Finally, the proposed method was applied to an actual footbridge structure to test its feasibility and effectiveness in real engineering scenarios. The experimental results showed that the proposed method could accurately identify bolt targets with a confidence level of over 80% and detect missing bolts under different image distances, perspective angles, light intensities, and image resolutions. Moreover, the experimental results on a footbridge demonstrated that the proposed method could reliably detect the missing bolt even at a shooting distance of 1 m. The proposed method provided a low-cost, efficient, and automated technical solution for the safety management of bolted connection components in engineering structures. Full article
(This article belongs to the Special Issue Recent Advances in Nondestructive Testing and Structural Health Monitoring)
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14 pages, 4395 KiB  
Article
Combined Acoustic Emission and Digital Image Correlation for Early Detection and Measurement of Fatigue Cracks in Rails and Train Parts under Dynamic Loading
by Alexander Machikhin, Anton Poroykov, Vladimir Bardakov, Artem Marchenkov, Daria Zhgut, Milana Sharikova, Vera Barat, Natalia Meleshko and Alexander Kren
Sensors 2022, 22(23), 9256; https://doi.org/10.3390/s22239256 - 28 Nov 2022
Cited by 4 | Viewed by 1328
Abstract
Fatigue crack in rails and cyclic-loaded train parts is a contributory factor in multiple railroad accidents. We address the problem of crack detection and measurement at early stages, when total failure has not yet occurred. We propose to combine acoustic emission (AE) testing [...] Read more.
Fatigue crack in rails and cyclic-loaded train parts is a contributory factor in multiple railroad accidents. We address the problem of crack detection and measurement at early stages, when total failure has not yet occurred. We propose to combine acoustic emission (AE) testing for prediction of crack growth with digital image correlation (DIC) for its accurate quantitative characterization. In this study, we imitated fatigue crack appearance and growth in samples of railway rail and two train parts by cyclic loading, and applied these two techniques for inspection. Experimental results clearly indicate the efficiency of AE in the early detection of fatigue cracks, and excellent DIC capabilities in terms of geometrical measurements. Combination of these techniques reveals a promising basis for real-time and non-destructive monitoring of rails and train parts. Full article
(This article belongs to the Special Issue Recent Advances in Nondestructive Testing and Structural Health Monitoring)
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