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Special Issue "Sensors for Nondestructive Testing and Evaluation"

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

Deadline for manuscript submissions: 30 May 2020.

Special Issue Editors

Prof. Dr. Bo Han
Website
Guest Editor
School of Civil Engineering, Shandong University, 17922 Jingshi Road, Jinan, 250061, China
Interests: non-destructive evaluation; tunneling structures; geotechnical structures; damage detection; health monitoring; AI health diagnosis
Prof. Dr. Seunghee Park
Website
Guest Editor
School of Civil, Architectural, and Environmental Engineering, Sungkyunkwan University, 2066, Seoburo, Jangangu, Suwon, Gyeonggido 16419, Korea
Interests: structural health monitoring; non-destructive evaluation; smart sensors; smart structures; damage detection
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive evaluation (NDE) is an engineering approach for examining the properties of a structure or system, without causing damage. Non-destructive evaluation techniques, such as optical, electromagnetics, ultrasonic, radiography, and thermal methods have contributed to ground-breaking improvements in safety in many industrial areas.
In order to develop successful NDE technology, various integrated technologies, such as advanced sensors, data measurement technology, signal processing method, and statistical decision making algorithms have been studied in combination, in order to evaluate the condition of the structures and machinery.
Meanwhile, over the last decades, there has been a growing number of new NDE solutions that provide artificial intelligence (AI) and machine learning (ML) based techniques for automated decision making. In addition, Internet of things (IoT) technologies are also undergoing great expansion and development, and the convergence of both AI and IoT are now realities that are going to change the paradigm of NDE technology.
We invite you to submit original research papers or technical or review articles to this Special Collection, with emphasis on novel and emerging technologies for a wide range of non-destructive evaluation techniques, including AI and ML combined techniques.

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

  • non-destructive evaluation
  • real-time monitoring
  • structural health monitoring
  • Data mining methods, algorithms, and applications
  • Data analysis for non-destructive evaluation
  • Advanced signal processing, data mining, and data fusion
  • Pattern recognition applications
  • Artificial intelligence and machine learning application for NDE
  • Computer vision-based NDE
  • Industry 4.0, sensors, and AI

Prof. Dr. Seunghee Park
Prof. Dr. Bo Han
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 papers will be 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 2000 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 evaluation;
  • structural health monitoring;
  • artificial intelligence;
  • machine learning;
  • Internet of things;
  • intelligent health diagnosis

Published Papers (7 papers)

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Research

Open AccessArticle
Test and Study of Pipe Pile Penetration in Cohesive Soil Using FBG Sensing Technology
Sensors 2020, 20(7), 1934; https://doi.org/10.3390/s20071934 - 30 Mar 2020
Abstract
In order to examine the applicability of Fiber Bragg Grating (FBG) sensing technology in the static penetration of pipe piles, static penetration tests in clay were conducted using double-wall open and closed model pipe piles. The strain was measured using FBG sensors, and [...] Read more.
In order to examine the applicability of Fiber Bragg Grating (FBG) sensing technology in the static penetration of pipe piles, static penetration tests in clay were conducted using double-wall open and closed model pipe piles. The strain was measured using FBG sensors, and the plug height was measured using a cable displacement sensor. Using one open pile and two closed piles, the difference in pipe pile penetration was compared and analyzed. Based on FBG sensing technology and the strain data, the penetration characteristics of the pipe pile, such as axial force, lateral friction, and driving resistance were examined. Results showed that FBG sensing technology has superior testing performance for the pipe pile penetration process, can accurately reflect the strain time history of pipe piles, and can clearly reflect the penetration process of pipe piles with increasing penetration depth. In addition, the variation law of the characteristics of the jacked pile pile–soil interface was obtained. This test has significance for model tests and the engineering design of pipe piles. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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Open AccessArticle
A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies
Sensors 2020, 20(7), 1829; https://doi.org/10.3390/s20071829 - 25 Mar 2020
Abstract
We explore a one-stage method for surface anomaly detection in industrial scenarios. On one side, encoder-decoder segmentation network is constructed to capture small targets as much as possible, and then dual background suppression mechanisms are designed to reduce noise patterns in coarse and [...] Read more.
We explore a one-stage method for surface anomaly detection in industrial scenarios. On one side, encoder-decoder segmentation network is constructed to capture small targets as much as possible, and then dual background suppression mechanisms are designed to reduce noise patterns in coarse and fine manners. On the other hand, a classification module without learning parameters is built to reduce information loss in small targets due to the inexistence of successive down-sampling processes. Experimental results demonstrate that our one-stage detector achieves state-of-the-art performance in terms of precision, recall and f-score. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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Open AccessArticle
A Model Test for the Influence of Lateral Pressure on Vertical Bearing Characteristics in Pile Jacking Process Based on Optical Sensors
Sensors 2020, 20(6), 1733; https://doi.org/10.3390/s20061733 - 20 Mar 2020
Abstract
Photoelectric integrated testing technology was used to study precast piles during pile jacking at the pile–soil interface considering the influence of the earth and pore water pressures on its vertical bearing performance. The low temperature sensitive fiber Bragg grating (FBG) strain sensors and [...] Read more.
Photoelectric integrated testing technology was used to study precast piles during pile jacking at the pile–soil interface considering the influence of the earth and pore water pressures on its vertical bearing performance. The low temperature sensitive fiber Bragg grating (FBG) strain sensors and miniature silicon piezoresistive sensors were implanted in the model pile to test the changes of earth pressure, pore water pressure and pile axial force of the jacked pile at the pile–soil interface, and the influence of lateral pressure on pile axial force was studied. The test results showed that the nylon rod is feasible as a model pile. The FBG strain sensor had a stable performance and monitored changes in the axial force of the model pile in real time. The miniature earth and pore water pressure sensors were small enough to avoid size effects and accurately measured changes in the earth and pore water pressures during the pile jacking process. During pile jacking, the lateral earth pressure increased gradually in depth, and the lateral earth pressure at the same depth tended to decrease at greater depths. Lateral pressures caused the axial force of the pile to increases by a factor of 1–2, where the maximum was 2.7. Therefore, the influence of the lateral pressure must be considered when studying the residual pile stress. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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Open AccessArticle
A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy
Sensors 2020, 20(6), 1586; https://doi.org/10.3390/s20061586 - 12 Mar 2020
Abstract
Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and [...] Read more.
Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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Open AccessArticle
Genetic Improvement of Sawn-Board Stiffness and Strength in Scots Pine (Pinus sylvestris L.)
Sensors 2020, 20(4), 1129; https://doi.org/10.3390/s20041129 - 19 Feb 2020
Abstract
Given an overall aim of improving Scots pine structural wood quality by selective tree breeding, we investigated the potential of non-destructive acoustic sensing tools to accurately predict wood stiffness (modulus of elasticity, MOE) and strength (modulus of rupture, MOR) of sawn boards. Non-destructive [...] Read more.
Given an overall aim of improving Scots pine structural wood quality by selective tree breeding, we investigated the potential of non-destructive acoustic sensing tools to accurately predict wood stiffness (modulus of elasticity, MOE) and strength (modulus of rupture, MOR) of sawn boards. Non-destructive measurements of wood density (DEN), acoustic velocity (VEL) and MOE were carried out at different stages of wood processing chain (standing trees, felled logs and sawn boards), whilst destructively measured stiffness and strength served as benchmark traits. All acoustic based MOE and VEL estimates proved to be good proxies (rA > 0.65) for sawn-board stiffness while MOETREE, VELHIT and resistograph wood density (DENRES) measured on standing trees and MOELOG and VELFAK measured on felled logs well reflected board strength. Individual-tree narrow-sense heritability ( h i 2 ) for VEL, MOE and MOR were weak (0.05–0.26) but were substantially stronger for wood density (0.34–0.40). Moreover, additive genetic coefficients of variation for MOE and MOR were in the range from 5.4% to 9.1%, offering potential targets for exploitation by selective breeding. Consequently, selective breeding based on MOETREE, DENRES or stem straightness (STR) could improve several structural wood traits simultaneously. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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Open AccessArticle
A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
Sensors 2020, 20(4), 1119; https://doi.org/10.3390/s20041119 - 18 Feb 2020
Abstract
Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 30 frames [...] Read more.
Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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Open AccessArticle
Characteristics Regarding Lift-Off Intersection of Pulse-Modulation Eddy Current Signals for Evaluation of Hidden Thickness Loss in Cladded Conductors
Sensors 2019, 19(19), 4102; https://doi.org/10.3390/s19194102 - 23 Sep 2019
Abstract
The cladded conductor is broadly utilized in engineering fields, such as aerospace, energy, and petrochemical; however, it is vulnerable to thickness loss occurring in the clad layer and nonconductive protection coating due to abrasive and corrosive environments. Such a flaw severely undermines the [...] Read more.
The cladded conductor is broadly utilized in engineering fields, such as aerospace, energy, and petrochemical; however, it is vulnerable to thickness loss occurring in the clad layer and nonconductive protection coating due to abrasive and corrosive environments. Such a flaw severely undermines the integrity and safety of the mechanical structures. Therefore, evaluating the thickness loss hidden inside cladded conductors via reliable nondestructive evaluation techniques is imperative. This paper intensively investigates the pulse-modulation eddy current technique (PMEC) for the assessment of thickness loss in a cladded conductor. An analytical model of the ferrite-cored probe is established for analyzing PMEC signals and characteristics of lift-off intersection (LOI) in testing signals. Experiments are conducted for evaluation of the thickness loss in cladded conductors. An inverse scheme based on LOI for estimation of the thickness-loss depth is proposed and further verified. Through simulations and experiments, it is found that the influences of the thickness loss in the clad layer and protective coating on the PMEC signals can be decoupled in virtue of the LOI characteristics. Based on LOI, the hidden thickness loss can be efficiently evaluated without much of a reduction in accuracy by using the PMEC probe for dedicated inspection of the cladded conductor. Full article
(This article belongs to the Special Issue Sensors for Nondestructive Testing and Evaluation)
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