Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Sensors for Non-Destructive Testing and Structural Health Monitoring
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors for Non-Destructive Testing and Structural Health Monitoring

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6002

Special Issue Editors

Dr. Xing Fu

E-Mail Website
Guest Editor
Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
Interests: cable force identification; data fusion; displacement reconstruction; structural health monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hong-Nan Li

E-Mail Website
Guest Editor
Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
Interests: theory and method of high-rise building structure design; structural vibration control; structural health monitoring; disaster prevention and mitigation for high-voltage transmission tower systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is to detect structural damage or degradation by analysing and evaluating the performance of a structural system through structural response using non-destructive sensing technology in the field. The process of structural health monitoring is to obtain the dynamic response measurement of the system timing sampling through a series of non-destructive testing sensors. Then, the characteristic factors sensitive to damage are extracted from these measured values, and these characteristic factors are statistically analysed to obtain the current health status of the structure. In the future, structure health monitoring technology should be transformed into automatic systems with minimal manual intervention, no damage to the structure, online real-time continuous monitoring, inspection and damage detection. In addition, the technology should enable the system to automatically report the status of the structure through the local area network or remote centre. Therefore, the identification accuracy of structural health monitoring systems is strongly dependent on non-destructive testing sensors and data processing and analysis algorithms.

The purpose of this Special Issue is to introduce the new generation of non-destructive testing sensors and mass data intelligent processing and analysis algorithms for structural health monitoring, with its scope including but not limited to the following topics:

  • The research and development of non-destructive testing sensors and supporting equipment;
  • Abnormal data diagnosis methods based on deep learning algorithms;
  • Structural damage identification methods based on computer vision technology;
  • Structural health monitoring methods and related devices based on digital twin;
  • Multi-source heterogeneous monitoring data fusion;
  • Health monitoring methods for large complex structural systems.

Dr. Xing Fu
Prof. Dr. Hong-Nan Li
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
  • damage identification
  • online monitoring
  • computer vision technology
  • deep learning
  • digital twin
  • data mining
  • data fusion

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

22 pages, 27285 KiB  
Article
The Effect of Annotation Quality on Wear Semantic Segmentation by CNN
by Mühenad Bilal, Ranadheer Podishetti, Leonid Koval, Mahmoud A. Gaafar, Daniel Grossmann and Markus Bregulla
Sensors 2024, 24(15), 4777; https://doi.org/10.3390/s24154777 - 23 Jul 2024
Viewed by 756
Abstract
In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and [...] Read more.
In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation. Full article
(This article belongs to the Special Issue Sensors for Non-Destructive Testing and Structural Health Monitoring)
►▼ Show Figures

Figure 1

16 pages, 5923 KiB  
Article
A Data-Mining Interpretation Method of Pavement Dynamic Response Signal by Combining DBSCAN and Findpeaks Function
by Hailong Liu, Ruqing Yao, Chunyi Cui and Jiuye Zhao
Sensors 2024, 24(3), 939; https://doi.org/10.3390/s24030939 - 31 Jan 2024
Viewed by 969
Abstract
During a heavy traffic flow featuring a substantial number of vehicles, the data reflecting the strain response of asphalt pavement under the vehicle load exhibit notable fluctuations with abnormal values, which can be attributed to the complex operating environment. Thus, there is a [...] Read more.
During a heavy traffic flow featuring a substantial number of vehicles, the data reflecting the strain response of asphalt pavement under the vehicle load exhibit notable fluctuations with abnormal values, which can be attributed to the complex operating environment. Thus, there is a need to create a real-time anomalous-data diagnosis system which could effectively extract dynamic strain features, such as peak values and peak separation from the large amount of data. This paper presents a dynamic response signal data analysis method that utilizes the DBSCAN clustering algorithm and the findpeaks function. This method is designed to analyze data collected by sensors installed within the pavement. The first step involves denoising the data using low-pass filters and other techniques. Subsequently, the DBSCAN algorithm, which has been improved using the K-Dist method, is used to diagnose abnormal data after denoising. The refined findpeaks function is further implemented to carry out the adaptive feature extraction of the denoised data which is free from anomalies. The enhanced DBSCAN algorithm is tested via simulation and illustrates its effectiveness while detecting abnormal data in the road dynamic response signal. The findpeaks function enables the relatively accurate identification of peak values, thus leading to the identification of strain signal peaks of complex multi-axle lorries. This study is valuable for efficient data processing and effective information utilization in pavement monitoring. Full article
(This article belongs to the Special Issue Sensors for Non-Destructive Testing and Structural Health Monitoring)
►▼ Show Figures

Figure 1

21 pages, 3551 KiB  
Article
Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis
by Xuan Zhang, Luyu Li and Gaoqiang Qu
Sensors 2024, 24(2), 505; https://doi.org/10.3390/s24020505 - 13 Jan 2024
Viewed by 1251
Abstract
In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of [...] Read more.
In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method’s advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems. Full article
(This article belongs to the Special Issue Sensors for Non-Destructive Testing and Structural Health Monitoring)
►▼ Show Figures

Figure 1

Review

Jump to: Research

18 pages, 3663 KiB  
Review
Railway Catenary Condition Monitoring: A Systematic Mapping of Recent Research
by Shaoyao Chen, Gunnstein T. Frøseth, Stefano Derosa, Albert Lau and Anders Rönnquist
Sensors 2024, 24(3), 1023; https://doi.org/10.3390/s24031023 - 5 Feb 2024
Viewed by 2049
Abstract
In this paper, a different approach to the traditional literature review—literature systematic mapping—is adopted to summarize the progress in the recent research on railway catenary system condition monitoring in terms of aspects such as sensor categories, monitoring targets, and so forth. Importantly, the [...] Read more.
In this paper, a different approach to the traditional literature review—literature systematic mapping—is adopted to summarize the progress in the recent research on railway catenary system condition monitoring in terms of aspects such as sensor categories, monitoring targets, and so forth. Importantly, the deep interconnections among these aspects are also investigated through systematic mapping. In addition, the authorship and publication trends are also examined. Compared to a traditional literature review, the literature mapping approach focuses less on the technical details of the research but reflects the research trends, and focuses in a specific field by visualizing them with the help of different plots and figures, which makes it more visually direct and comprehensible than the traditional literature review approach. Full article
(This article belongs to the Special Issue Sensors for Non-Destructive Testing and Structural Health Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop