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Advanced Sensors in Nondestructive Testing and Structural Health Monitoring

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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: 25 April 2025 | Viewed by 15990

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Special Issue Editor

Dr. Miguel A. Machado

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Guest Editor

Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Lisboa, Portugal
Interests: non-destructive testing; eddy currents; thermography; sensors; structural health monitoring

Special Issue Information

Dear Colleagues,

Advanced sensors play a crucial role in enhancing the accuracy, reliability, and efficiency of Nondestructive Testing (NDT) and Structural Health Monitoring (SHM). Traditional methods are often time-consuming, subjective, and may be inadequate to detect hidden defects or early signs of damage. In contrast, advanced sensors enable real-time, remote, and nonintrusive monitoring, allowing for early detection of structural issues and facilitating proactive maintenance and repair strategies. Collecting and analyzing data from advanced sensors provides valuable insights into the structural health and performance of critical assets such as bridges, pipelines, aircraft, and buildings.

The scope of this Special Issue encompasses various technologies used in advanced sensors, including but not limited to optical fibers, piezoelectric materials, wireless sensor networks, ultrasound, electromagnetic waves, and smart materials. We invite submissions that explore the design, development, simulation, and validation of advanced sensors. Additionally, we encourage researchers to share their work on practical applications of advanced sensors in SHM, including case studies that demonstrate their effectiveness in real-world scenarios. Both reviews and original research articles are welcome, contributing to the advancement of knowledge and implementation in this field.

Authors are encouraged to address topics such as: Topics include, but are not limited to, the following:

Design and simulation of advanced sensors;
Case studies and practical applications showcasing the use of advanced sensors in SHM;
Integration of advanced sensor technologies into existing NDT and SHM frameworks;
Data analysis and interpretation methods for extracting valuable insights from sensor data;
Proactive maintenance and repair strategies enabled by advanced sensor monitoring.

Dr. Miguel A. Machado
Guest Editor

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Keywords

advanced sensors
Nondestructive Testing (NDT)
Structural Health Monitoring (SHM)
sensor technologies
smart materials
numerical simulation
remote monitoring
proactive maintenance
structural integrity
real-time monitoring
wireless sensor networks
terahertz Inspection
eddy currents
ultrasounds
thermography

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Published Papers (10 papers)

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Research

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14 pages, 6235 KiB

Open AccessArticle

Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study

by Riccardo Mennilli, Luigi Mazza and Andrea Mura

Sensors 2025, 25(2), 537; https://doi.org/10.3390/s25020537 - 17 Jan 2025

Viewed by 441

Abstract

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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23 pages, 16904 KiB

Open AccessArticle

Novel Visualization of Building Earthquake Response Recorded by a Dense Network of Sensors

by Lichiel Cruz, Maria I. Todorovska, Mihailo D. Trifunac, Alimu Aihemaiti, Guoliang Lin and Jianwen Cui

Sensors 2025, 25(2), 417; https://doi.org/10.3390/s25020417 - 12 Jan 2025

Viewed by 484

Abstract

The strong motion records collected in full-scale structures provide the ultimate evidence of how real structures, in situ, respond to earthquakes. This paper presents a novel method for visualization, in three dimensions (3D), of the collective motion recorded by a dense array of sensors in a building. The method is based on one- and two-dimensional biharmonic spline interpolation of the motion recorded by multiple sensors on the same or multiple floors. It is demonstrated on novel data that have been recorded recently in a 50-story skyscraper, uniquely instrumented with multiple triaxial accelerometers per floor, approximately at every five floors above ground and at two basement levels, and with rotational seismometers and two borehole arrays measuring the motion of the soil very near the building foundation. The method is computationally efficient and suitable for real-time application and rapid assessment of structural health. The animations provide invaluable insight into the 3D structural response of the building as a whole, including wave propagation through the structure and the interplay between translations and rotations, which will be useful for testing existing and developing new methods for structural health monitoring of buildings and for the further development of building design codes. Animations of selected earthquakes can be found on YouTube at @TPYC-seismic. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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16 pages, 6650 KiB

Open AccessArticle

An Experimental Assessment Using Acoustic Emission Sensors to Effectively Detect Surface Deterioration on Steel Plates

by Nikolaos Angelopoulos and Vassilios Kappatos

Sensors 2024, 24(19), 6462; https://doi.org/10.3390/s24196462 - 6 Oct 2024

Cited by 1 | Viewed by 1393

Abstract

Acoustic emission (AE) testing is used for the continuous evaluation of structural integrity and the monitoring of damage evolution in structural components and materials. During operation, the environmental and loading conditions of metal structures can result in corrosion and surface wear damage. The early detection of surface degradation flaws is crucial, as they can serve as local stress concentration points, leading to crack initiation and failure. In this work, the effectiveness of AE in monitoring corrosion and surface wear flaw formation was experimentally evaluated. AE sensors were installed on steel test plates during the artificial induction of corrosion and surface wear in order to detect and record the generated AE signals. Corrosion-related AE signals typically exhibit low amplitude, count, and energy values. The direct detection of active corrosion can be challenging in noisy environments, but it can be carried out under certain conditions using dedicated AE sensor groups. Surface-wear-related AE signals exhibit high amplitude, energy, and count values, with long duration values that are associated with wear and grinding conditions. It was found that AE sensors can be utilised to detect corrosion and surface degradation events. The effectiveness of the AE method in detecting surface degradation in noisy environments can be improved by implementing a filtering methodology. This will limit the recording of noise-related signals that can mask out actual surface degradation AE events. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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16 pages, 5560 KiB

Open AccessArticle

On-Line Measurement of Tracking Poses of Heliostats in Concentrated Solar Power Plants

by Fen Xu, Changhao Li and Feihu Sun

Sensors 2024, 24(19), 6373; https://doi.org/10.3390/s24196373 - 1 Oct 2024

Viewed by 810

Abstract

The tracking pose of heliostats directly affects the stability and working efficiency of concentrated solar power (CSP) plants. Due to occlusion, over-exposure, and uneven illumination caused by mirror reflection, traditional image processing algorithms showed poor performances on the detection and segmentation of heliostats, which impede vision-based 3D measurement of tracking poses of heliostats. To tackle this issue, object detection using deep learning neural networks are exploited. An improved neural network based on YOLO-v5 framework has been designed to solve the on-line detection problem of heliostats. The model achieves a recognition accuracy of 99.7% for the test set, outperforming traditional methods significantly. Based on segmented results, the corner points of each heliostat are found out using Hough Transform and line intersection methods. The 3D poses of each heliostat are then solved out based on the image coordinates of specific feature points and the camera model. Experimental and field test results demonstrate the feasibility of this hybrid approach, which provides a low-cost solution for the monitoring and measurement of tracking poses of the heliostats in CSP. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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19 pages, 5558 KiB

Open AccessArticle

Convolution Neural Network Development for Identifying Damage in Vibrating Pylons with Mass Attachments

by George D. Manolis and Georgios I. Dadoulis

Sensors 2024, 24(19), 6255; https://doi.org/10.3390/s24196255 - 27 Sep 2024

Viewed by 676

Abstract

A convolution neural network (CNN) is developed in this work to detect damage in pylons by measuring their vibratory response. More specifically, damage detection through testing relies on the development of damage-sensitive indicators, which are then used to reach a decision regarding the existence/absence of damage, provided they have been retrieved from at least two distinct structural states. Damage indicators, however, exhibit a relatively low sensitivity regarding the onset of structural damage, further exacerbated by the low amplitude response to a variety of environmentally induced loads. To this end, a mathematical model is developed to interpret the experimental data recovered from a fixed-base pylon with a top mass attachment to transverse motion. Damage is introduced in the mathematical model in the form of springs corresponding to the cracking of the beam’s lower end. Families of numerically generated acceleration records are produced at select stations along the beam’s height, which are then used for training a CNN. Once trained, it is used to identify damage from acceleration records produced from a series of experiments. Difficulties faced by CNN in correctly identifying the presence/absence of damage in the pylon are discussed, and steps taken to improve the quality of the results are proposed. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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15 pages, 5914 KiB

Open AccessCommunication

Shear Wave Velocity Determination of a Complex Field Site Using Improved Nondestructive SASW Testing

by Gunwoong Kim and Sungmoon Hwang

Sensors 2024, 24(10), 3231; https://doi.org/10.3390/s24103231 - 19 May 2024

Cited by 1 | Viewed by 1418

Abstract

The nondestructive spectral analysis of surface waves (SASW) technique determines the shear wave velocities along the wide wavelength range using Rayleigh-type surface waves that propagate along pairs of receivers on the surface. The typical configuration of source-receivers consists of a vertical source and three vertical receivers arranged in a linear array. While this approach allows for effective site characterization, laterally variable sites are often challenging to characterize. In addition, in a traditional SASW test configuration system, where sources are placed in one direction, the data are collected more on one side, which can cause an imbalance in the interpretation of the data. Data interpretation issues can be resolved by moving the source to opposite ends of the original array and relocating receivers to perform a second complete set of tests. Consequently, two different Vs profiles can be provided with only a small amount of additional time at sites where lateral variability exists. Furthermore, the testing procedure can be modified to enhance the site characterization during data collection. The advantages of performing SASW testing in both directions are discussed using a real case study. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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18 pages, 37636 KiB

Open AccessArticle

Transversal Displacement Detection of an Arched Bridge with a Multimonostatic Multiple-Input Multiple-Output Radar

by Lorenzo Pagnini, Lapo Miccinesi, Alessandra Beni and Massimiliano Pieraccini

Sensors 2024, 24(6), 1839; https://doi.org/10.3390/s24061839 - 13 Mar 2024

Cited by 1 | Viewed by 1109

Abstract

Interferometric radars are widely used for monitoring civil structures. Bridges are critical structures that need to be constantly monitored for the safety of the users. In this work, a frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar was used for monitoring an arched bridge in Catanzaro, Italy. Two measurements were carried out; a first standard measurement was made in a monostatic configuration, while a subsequent measurement was carried out in a multimonostatic configuration in order to retrieve the components of the deck displacement. A method that is able to predict the measurement uncertainty as a function of the multimonostatic geometry is provided, thereby aiming to facilitate the operators in the choice of the proper experimental setup. The multimonostatic measurement revealed a displacement along the horizontal direction that was four times higher than the one along the vertical direction, while the values reported in the literature correspond to a ratio of at most around 0.2. This is the first time that such a large ratio detected by radar has been reported; at any rate, it is compatible with the arched structure of this specific bridge. This case study highlights the importance of techniques that are able to retrieve at least two components of the displacement. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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12 pages, 6335 KiB

Open AccessArticle

Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR

by Min Zhao, Zehao Fang, Ning Ding, Nan Li, Tengfei Su and Huihuan Qian

Sensors 2023, 23(24), 9761; https://doi.org/10.3390/s23249761 - 11 Dec 2023

Cited by 1 | Viewed by 1482

Abstract

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness. Our proposed method leverages laser point cloud data and employs a 3D scanner for objective detection of geometric deformations in underground pipe corridors. By utilizing this approach, we enable a quantitative assessment of blockage levels, offering a significant improvement over traditional CCTV-based methods. The key advantages of our method lie in its objectivity and quantification capabilities, ultimately enhancing detection reliability, accuracy, and overall inspection efficiency. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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19 pages, 7944 KiB

Open AccessArticle

Damage Monitoring of Braided Composites Using CNT Yarn Sensor Based on Artificial Fish Swarm Algorithm

by Hongxia Wang, Yungang Jia, Minrui Jia, Xiaoyuan Pei and Zhenkai Wan

Sensors 2023, 23(16), 7067; https://doi.org/10.3390/s23167067 - 10 Aug 2023

Cited by 1 | Viewed by 1313

Abstract

This study aims to enable intelligent structural health monitoring of internal damage in aerospace structural components, providing a crucial means of assuring safety and reliability in the aerospace field. To address the limitations and assumptions of traditional monitoring methods, carbon nanotube (CNT) yarn sensors are used as key elements. These sensors are woven with carbon fiber yarns using a three-dimensional six-way braiding process and cured with resin composites. To optimize the sensor configuration, an artificial fish swarm algorithm (AFSA) is introduced, simulating the foraging behavior of fish to determine the best position and number of CNT yarn sensors. Experimental simulations are conducted on 3D braided composites of varying sizes, including penetration hole damage, line damage, and folded wire-mounted damage, to analyze the changes in the resistance data of carbon nanosensors within the damaged material. The results demonstrate that the optimized configuration of CNT yarn sensors based on AFSA is suitable for damage monitoring in 3D woven composites. The experimental positioning errors range from 0.224 to 0.510 mm, with all error values being less than 1 mm, thus achieving minimum sensor coverage for a maximum area. This result not only effectively reduces the cost of the monitoring system, but also improves the accuracy and reliability of the monitoring process. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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Review

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43 pages, 6234 KiB

Open AccessReview

Eddy Currents Probe Design for NDT Applications: A Review

by Miguel A. Machado

Sensors 2024, 24(17), 5819; https://doi.org/10.3390/s24175819 - 7 Sep 2024

Viewed by 5332

Abstract

Eddy current testing (ECT) is a crucial non-destructive testing (NDT) technique extensively used across various industries to detect surface and sub-surface defects in conductive materials. This review explores the latest advancements and methodologies in the design of eddy current probes, emphasizing their application in diverse industrial contexts such as aerospace, automotive, energy, and electronics. It explores the fundamental principles of ECT, examining how eddy currents interact with material defects to provide valuable insights into material integrity. The integration of numerical simulations, particularly through the Finite Element Method (FEM), has emerged as a transformative approach, enabling the precise modeling of electromagnetic interactions and optimizing probe configurations. Innovative probe designs, including multiple coil configurations, have significantly enhanced defect detection capabilities. Despite these advancements, challenges remain, particularly in calibration and sensitivity to environmental conditions. This comprehensive overview highlights the evolving landscape of ECT probe design, aiming to provide researchers and practitioners with a detailed understanding of current trends in this dynamic field. Full article

(This article belongs to the Special Issue Advanced Sensors in Nondestructive Testing and Structural Health Monitoring)

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