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Advanced Sensing Technology for Structural Health Monitoring
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Advanced Sensing Technology for Structural Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 4183

Special Issue Editors

Dr. Shang Gao

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: wireless sensor networks; structural health monitoring; shock wave monitoring
Prof. Dr. Xuewu Dai

E-Mail Website
Guest Editor
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
Interests: networked control and optimal scheduling; robust adaptive signal processing; state estimation; machine learning for scheduling; applications to industrial internet of things; condition monitoring; precise time synchronization; wireless communication and wireless networked control system; intelligent railway transportation; train traffic scheduling
Dr. Xiaotian Chen

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Sichuan University, Chengdu, China
Interests: structural health monitoring; non-destructive testing; tomography; sensor platform development based on Mixed Reality (MR)

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) aims to develop automated systems for the continuous monitoring, inspection, and damage detection of structures with minimum labour involvement. However, there are several challenges in creating such advanced sensing technologies for monitoring various physical or chemical parameters related to the health and durable service life of structures. Therefore, this Special Issue aims to present new ideas and experimental results in the field of advanced sensing technology theory which might contribute to its practical application.

This Special Issue will publish high-quality original research papers in the following relevant and interrelated fields, including, but not limited to, detection diagnosis, damage detection and imaging, fiber optic sensors, piezoelectric sensors, magnetostrictive sensors, strain sensors, pressure sensors, microwave sensors, and instrumentation and measurement. Non-destructive testing is also another topic of interest.

Dr. Shang Gao
Prof. Dr. Xuewu Dai
Dr. Xiaotian Chen
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. Applied Sciences 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 2400 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
  • smart sensor
  • detection diagnosis
  • damage detection and imaging
  • fiber optic sensors
  • piezoelectric sensors
  • magnetostrictive sensors
  • strain sensors
  • pressure sensors
  • microwave sensors

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Published Papers (4 papers)

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Research

17 pages, 9478 KiB  
Article
Characterization of Multi-Layer Rolling Contact Fatigue Defects in Railway Rails Using Sweeping Eddy Current Pulse Thermal-Tomography
by Hengbo Zhang, Shudi Zhang, Xiaotian Chen, Yingying Li, Yiling Zou and Yizhao Zeng
Appl. Sci. 2024, 14(16), 7269; https://doi.org/10.3390/app14167269 - 19 Aug 2024
Viewed by 825
Abstract
Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting [...] Read more.
Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Structural Health Monitoring)
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37 pages, 24321 KiB  
Article
Damage Identification of Plate Structures Based on a Non-Convex Approximate Robust Principal Component Analysis
by Dong Liang, Yarong Zhang, Xueping Jiang, Li Yin, Ang Li and Guanyu Shen
Appl. Sci. 2024, 14(16), 7076; https://doi.org/10.3390/app14167076 - 12 Aug 2024
Viewed by 773
Abstract
Structural damage identification has been one of the key applications in the field of Structural Health Monitoring (SHM). With the development of technology and the growth of demand, the method of identifying damage anomalies in plate structures is increasingly being developed in pursuit [...] Read more.
Structural damage identification has been one of the key applications in the field of Structural Health Monitoring (SHM). With the development of technology and the growth of demand, the method of identifying damage anomalies in plate structures is increasingly being developed in pursuit of accuracy and high efficiency. Principal Component Analysis (PCA) has always been effective in damage identification in SHM, but because of its sensitivity to outliers and low robustness, it does not work well for complex damage or data. The effect is not satisfactory. This paper introduces the Robust Principal Component Analysis (RPCA) model framework for the characteristics of PCA that are too sensitive to the outliers or noise in the data and combines it with Lamb to achieve the damage recognition of wavefield images, which greatly improves the robustness and reliability. To further improve the real-time monitoring efficiency and reduce the error, this paper proposes a non-convex approximate RPCA (NCA-RPCA) algorithm model. The algorithm uses a non-convex rank approximation function to approximate the rank of the matrix, a non-convex penalty function to approximate the norm to ensure the uniqueness of the sparse solution, and an alternating direction multiplier method to solve the problem, which is more efficient. Comparison and analysis with various algorithms through simulation and experiments show that the algorithm in this paper improves the real-time monitoring efficiency by about ten times, the error is also greatly reduced, and it can restore the original data at a lower rank level to achieve more effective damage identification in the field of SHM. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Structural Health Monitoring)
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23 pages, 7066 KiB  
Article
Research on Time-Reversal Focusing Imaging Method to Evaluate a Multi-Layer Armor Composite Structure
by Jian Jiang, Linfang Qian and Yadong Xu
Appl. Sci. 2024, 14(13), 5537; https://doi.org/10.3390/app14135537 - 26 Jun 2024
Viewed by 998
Abstract
Armor composite structures have attracted interest in structural health monitoring (SHM) for their applications in damage localization. The signal propagation and the frequency dispersion features of the Lamb wave signal on thick armor composite structures are more complicated than their counterparts on other [...] Read more.
Armor composite structures have attracted interest in structural health monitoring (SHM) for their applications in damage localization. The signal propagation and the frequency dispersion features of the Lamb wave signal on thick armor composite structures are more complicated than their counterparts on other composite plates. In this research, a time-reversal localization and imaging method for impact localization of armor composite structures is proposed. First, composite sandwich structures were designed that are typically composed of ballistic-resistant ceramic materials as the face panel and a composite material as the core layer, sandwiched between metal materials serving as the backplate. The results show that the proposed method can validate the position of impact efficiently, and radial error is within 4.12 mm and 5.39 mm in single-damage and multi-damage imaging localization, respectively. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Structural Health Monitoring)
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22 pages, 6072 KiB  
Article
Triaxial Load Cell for Ergonomic Risk Assessment: A Study Case of Applied Force of Thumb
by Mario Acosta-Flores, Martha Roselia Contreras-Valenzuela, J. Guadalupe Velásquez-Aguilar, Francisco Cuenca-Jiménez and Marta Lilia Eraña-Díaz
Appl. Sci. 2024, 14(10), 3981; https://doi.org/10.3390/app14103981 - 8 May 2024
Viewed by 905
Abstract
To assess the ergonomic risk level in work systems involving tasks performed with hands or fingers, it is necessary to know the exerted triaxial forces. To address this need, a prototype of a triaxial load cell based on principles of linear elasticity theory [...] Read more.
To assess the ergonomic risk level in work systems involving tasks performed with hands or fingers, it is necessary to know the exerted triaxial forces. To address this need, a prototype of a triaxial load cell based on principles of linear elasticity theory and mechanical problems of torsion, bending and axial load is presented. This work includes an analytical strain model for each instrumented point and its solution regarding the applied force to a triaxial load cell. The proposed load cell was calibrated and validated by performing different static experimental tests. As a case study, the applied force in three directions while the thumb activates a cigarette lighter was measured. Triaxial forces and resultant forces were obtained and compared with the parameter of 10 N established by the ergonomic standards as reference values for pressing down with the thumb, finding that the applied forces in eight tests were 23.73 N, 43.51 N, 12.69 N, 14.50 N 20.35 N, 21.67 N, 39.74 N and 46.02 N, exceeding the reference values and establishing a direct relationship with Quervain syndrome. In conclusion, the developed load cell is a valid and reliable alternative to measure many forces that cannot be obtained with commercial devices, allowing the level of ergonomic risk to be determined with great precision. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Structural Health Monitoring)
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