A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani, Sahar; Dackermann, Ulrike

doi:10.3390/s23042204

Cited by 93 publications

(49 citation statements)

References 431 publications

(307 reference statements)

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“…Table 4. Typical EOCs and appropriate sensors for SHM systems [141]. Selecting the most appropriate sensor based on the system requirements and limitations depends on assessing specific sensor characteristics, such as (1) susceptibility to damage, (2) susceptibility to noise, (3) susceptibility to variations in the EOC, (4) susceptibility to chemical influences, (5) susceptibility to mechanical influences, (6) measurement accuracy, (7) error-proneness, and (8) cost.…”

Section: Sensing Technologiesmentioning

confidence: 99%

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

et al. 2023

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The development of Structural Health Monitoring (SHM) and Non-Destructive Testing (NDT) techniques has rapidly evolved and matured over the past few decades. Advances in sensor technology have facilitated deploying SHM systems for large-scale structures and local NDT of structural members. Although both methods have been successfully applied to identify structural damage in various systems, Environmental and Operational Condition (EOC) variations can influence sensor measurements and mask damage signatures in the structural response. EOCs include environmental conditions, such as temperature, humidity, and wind, as well as operational conditions, such as mass loading, vibration, and boundary conditions. The effect of EOCs can significantly undermine the reliability and robustness of damage assessment technologies and limit their performance. Thus, successful SHM and NDT systems can compensate for changing EOCs. This paper provides a state-of-the-art review of the effects of EOCs on SHM and NDT systems. It presents recent developments in advanced sensing technology, signal processing, and analysis techniques that aim to eliminate the masking effect of EOC variations and increase the damage sensitivity and performance of SHM and NDT systems. The paper concludes with current research challenges, trends, and recommendations for future research directions.

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Section: Sensing Technologiesmentioning

confidence: 99%

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

et al. 2023

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“…Hence, acquiring an X-ray image of the exact same component at different source-to-detector distances (SDD) will yield varying intensity values. As the X-rays transmit through a part under test, the incident intensity is attenuated, as mathematically represented in equation (1).…”

Section: How Exposure Factors Affect Image Qualitymentioning

confidence: 99%

“…Non-Destructive Testing (NDT) involves a vast range of inspection methods used to assess the quality and integrity of materials, components, and structures without causing damage or compromising their functionality [1]. Through adequate application of NDT methods on components under test, flaws are detected and evaluated to ascertain if they constitute a defect that could impact a component's fitness-for-use [2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Hena¹,

Wei²,

Ibarra-Castanedo³

et al. 2023

Preprint

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In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

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“…Suitable SHM sensors are also used in other fields, such as construction progress monitoring, structural design, safety risk assessment, maintenance management, and smart operations. A comprehensive review of conventional sensor systems can be found in [ 6 ], and of advanced sensor systems in [ 7 , 8 ]. Data analysis: The recorded sensor data typically undergoes a process of data acquisition, signal conditioning, data transfer, data storage, signal processing, and data interpretation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani

Dackermann

2023

Sensors

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In recent decades, structural health monitoring (SHM) has gained increased importance for ensuring the sustainability and serviceability of large and complex structures. To design an SHM system that delivers optimal monitoring outcomes, engineers must make decisions on numerous system specifications, including the sensor types, numbers, and placements, as well as data transfer, storage, and data analysis techniques. Optimization algorithms are employed to optimize the system settings, such as the sensor configuration, that significantly impact the quality and information density of the captured data and, hence, the system performance. Optimal sensor placement (OSP) is defined as the placement of sensors that results in the least amount of monitoring cost while meeting predefined performance requirements. An optimization algorithm generally finds the “best available” values of an objective function, given a specific input (or domain). Various optimization algorithms, from random search to heuristic algorithms, have been developed by researchers for different SHM purposes, including OSP. This paper comprehensively reviews the most recent optimization algorithms for SHM and OSP. The article focuses on the following: (I) the definition of SHM and all its components, including sensor systems and damage detection methods, (II) the problem formulation of OSP and all current methods, (III) the introduction of optimization algorithms and their types, and (IV) how various existing optimization methodologies can be applied to SHM systems and OSP methods. Our comprehensive comparative review revealed that applying optimization algorithms in SHM systems, including their use for OSP, to derive an optimal solution, has become increasingly common and has resulted in the development of sophisticated methods tailored to SHM. This article also demonstrates that these sophisticated methods, using artificial intelligence (AI), are highly accurate and fast at solving complex problems.

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A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Cited by 93 publications

References 431 publications

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

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