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Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation

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

Deadline for manuscript submissions: 20 October 2024 | Viewed by 5365

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Special Issue Editors

Prof. Dr. Yuan Yao

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Guest Editor

Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
Interests: nondestructive testing data analysis; process data analytics; multivariate analysis; machine learning; process monitoring; soft sensors
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gilbert-Rainer Gillich

E-Mail Website
Guest Editor

Department of Engineering Science, Babeș-Bolyai University Cluj-Napoca, 320085 Resita, Romania
Interests: vibration; damage detection; signal analysis; structural engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive testing and evaluation (NDT and E) plays an important role in investigating material or structure properties without causing damage. This method has been widely applied in various sectors of industry, agriculture, medicine, and environmental protection. In the era of big data, the internet of things, and artificial intelligence, more advanced NDT techniques have been developed, and their applications have been extensively explored. Meanwhile, data science technologies—including state-of-the-art deep learning methods and high-fidelity numerical simulations—have been utilized for processing and analysis of data collected from NDT experiments.

This Special Issue welcomes submissions of both review and original research articles on the recent progress related to the development, applications, and data analysis of NDT techniques.

Prof. Dr. Yuan Yao
Prof. Dr. Gilbert-Rainer Gillich
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

non-destructive testing
sensor technology
NDT data analysis
numerical simulations
acoustic emission testing (AE or AT)
acoustic microscopy
electromagnetic testing (ET) or electromagnetic inspection (commonly known as "EMI")
guided wave testing (GWT)
impulse excitation technique (IET)
microwave imaging
nondestructive testing of packaged items
X-ray, optical and terahertz image of a packaged IC
terahertz nondestructive evaluation (THz)
infrared and thermal testing (IR)
laser testing
leak testing (LT) or leak detection
machine vision based automatic inspection
magnetic resonance imaging (MRI) and NMR spectroscopy
metallographic replicas
spectroscopy
optical microscopy
positive material identification (PMI)
radiographic testing (RT) (see also industrial radiography and radiography)
resonant inspection
scanning electron microscopy
surface temper etch (Nital Etch)
ultrasonic testing (UT)
vibration analysis
visual inspection (VT)
weight and load testing of structures
corroscan/C-scan
3D computed tomography
heat exchanger life assessment system
RTJ flange special ultrasonic testing

Published Papers (6 papers)

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Research

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13 pages, 7652 KiB

Open AccessArticle

A Hardware Encoder-Based Synchronization Method for a Fast Terahertz TDS Imaging System Based on the ECOPS Scheme

by Marcin Maciejewski, Kamil Kamiński and Norbert Pałka

Sensors 2024, 24(6), 1806; https://0-doi-org.brum.beds.ac.uk/10.3390/s24061806 - 11 Mar 2024

Viewed by 567

Abstract

In this paper, we report our use of a hardware encoder-based synchronization method for a fast terahertz time-domain spectroscopy raster scanner built with the commercially available TeraFlash Smart platform. We describe the principles of our method, including our incorporation of synchronization signals from various devices included in the scanner. We also describe its implementation in a microcontroller with a dedicated counter. By such means, a fast scanning mode was obtained, which was 35 times faster than a traditional step-by-step approach. To validate the proposed synchronization method, we carried out measurements using the USAF 1951 resolution test and a fiberglass plate with a set of intentionally introduced defects. Our results confirmed that the TDS scanner with the developed synchronization method was able to capture high-quality images with resolutions as high as those obtained using traditional step-by-step scanning, but with significantly reduced scanning times. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation)

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22 pages, 1135 KiB

Open AccessArticle

Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign

by Dapeng Jiang, Keqi Wang, Hongbo Li and Yizhuo Zhang

Sensors 2024, 24(4), 1245; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041245 - 15 Feb 2024

Viewed by 604

Abstract

This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation)

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17 pages, 17087 KiB

Open AccessArticle

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

by Damira Smagulova, Bengisu Yilmaz and Elena Jasiuniene

Sensors 2024, 24(1), 176; https://0-doi-org.brum.beds.ac.uk/10.3390/s24010176 - 28 Dec 2023

Cited by 1 | Viewed by 644

Abstract

Ultrasonic non-destructive evaluation in pulse-echo mode is used for the inspection of single-lap aluminum adhesive joints, which contain interface defects in bonding area. The aim of the research is to increase the probability of defect detection in addition to ensuring that the defect sizes are accurately estimated. To achieve this, this study explores additional ultrasonic features (not only amplitude) that could provide more accurate information about the quality of the structure and the presence of interface defects. In this work, two types of interface defects, namely inclusions and delaminations, were studied based on the extracted ultrasonic features in order to evaluate the expected feasibility of defect detection and the evaluation of its performance. In addition, an analysis of multiple interface reflections, which have been proved to improve detection in our previous works, was applied along with the extraction of various ultrasonic features, since it can increase the probability of defect detection. The ultrasonic features with the best performance for each defect type were identified and a comparative analysis was carried out, showing that it is more challenging to size inclusion-type defects compared to delaminations. The best performance is observed for the features such as peak-to-peak amplitude, ratio coefficients, absolute energy, absolute time of flight, mean value of the amplitude, standard deviation value, and variation coefficient for both types of defects. The maximum relative error of the defect size compared to the real one for these features is 16.9% for inclusions and 3.6% for delaminations, with minimum errors of 11.4% and 2.2%, respectively. In addition, it was determined that analysis of the data from repetitive reflections from the sample interface, namely, the aluminum-adhesive second and third reflections, that these contribute to an increase in the probability of defect detection. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation)

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22 pages, 5363 KiB

Open AccessArticle

Characterizing Mechanical Properties of Layered Engineered Wood Using Guided Waves and Genetic Algorithm

by Nemish Atreya, Pai Wang and Xuan Zhu

Sensors 2023, 23(22), 9184; https://0-doi-org.brum.beds.ac.uk/10.3390/s23229184 - 14 Nov 2023

Viewed by 849

Abstract

This study develops a framework for determining the material parameters of layered engineered wood in a nondestructive manner. The motivation lies in enhancing nondestructive evaluation (NDE) and quality assurance (QA) for engineered wood or mass timber, promising construction materials for sustainable and resilient civil structures. The study employs static compression tests, guided wave measurements, and a genetic algorithm (GA) to solve the inverse problem of determining the mechanical properties of a laminated veneer lumber (LVL) bar. Miniature LVL samples are subjected to compression tests to derive the elastic moduli and Poisson’s ratios. Due to the intrinsic heterogeneity, the destructive compression tests yield large coefficients of variances ranging from 2.5 to 73.2%. Dispersion relations are obtained from spatial–temporal sampling of dynamic responses of the LVL bar. The GA pinpoints optimal mechanical properties by updating orthotropic elastic constants of the LVL material, and thereby dispersion curves, in a COMSOL simulation in accordance with experimental dispersion relations. The proposed framework can support estimation accuracy with errors less than 10% for most elastic constants. Focusing on vertical flexural modes, the estimated elastic constants generally resemble reference values from compression tests. This is the first study that evaluates the feasibility of using guided waves and multi-variable optimization to gauge the mechanical traits of LVL and establishes the foundation for further advances in the study of layered engineered wood structures. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation)

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14 pages, 3244 KiB

Open AccessArticle

Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns

by Yi Liu, Yuxin Jiang, Zengliang Gao, Kaixin Liu and Yuan Yao

Sensors 2023, 23(5), 2658; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052658 - 28 Feb 2023

Viewed by 1396

Abstract

In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation)

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Review

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37 pages, 3927 KiB

Open AccessReview

Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art

by Narges Pahnabi, Thomas Schumacher and Arijit Sinha

Sensors 2024, 24(9), 2901; https://0-doi-org.brum.beds.ac.uk/10.3390/s24092901 - 1 May 2024

Viewed by 565

Abstract

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR’s capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material’s complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Non-destructive Testing and Evaluation)

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