Ndt & E International最新文献

{"title":"RF antenna array for contactless structural health monitoring: Ultrasonic benchmarking and application to airfoil structure","authors":"Deepak Kumar ,&nbsp;Yogesh Kumar Yadav ,&nbsp;Sahil Kalra ,&nbsp;Prabhat Munshi","doi":"10.1016/j.ndteint.2026.103643","DOIUrl":"10.1016/j.ndteint.2026.103643","url":null,"abstract":"<div><div>Sensors are the primary component in the study of health monitoring of various structures. Numerous cutting-edge smart sensors have been utilized to improve monitoring technologies, however, the necessity to patch them to the structure in close contact still creates major complications in their actual deployment. Moreover, the contact sensors add a mass penalty to the structural element, causing a challenge for thin and flexible structures. In this paper, we introduce a contactless approach for damage localization in metallic plates using ultra-wide-band (UWB) antennas to overcome the limitations of contact-based approaches. The UWB antenna array is placed at a distance from the structure and is used to transmit and receive electromagnetic (EM) waves in the radio frequency (RF) range. Additionally, an imaging algorithm is developed to locate the damage in the structure. The simulation and experimental results demonstrate that the algorithm accurately estimates the damage locations. Furthermore, the estimated results of the proposed RF-based approach are comparatively validated with the existing ultrasonic sensor-based contact approach. Our simulation and experimental results show that both techniques (ultrasonic and RF) have a par accuracy of 99.93% for damage localization with respect to actual damage locations. The comparative study confirms that the UWB antennas are equally efficient in multi-damage localization in metallic plates, with the additional advantage of eliminating sensor patching onto the structure. This leads to the conviction that the UWB antennas are a novel addition to contactless SHM for various metallic structures. The technique is further extended to non-metallic airfoil structures for damage localization, and the computed accuracy of located damage is 95.1% with respect to actual damage location.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103643"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Method and application of data conversion between modulated and flash thermal imaging","authors":"Wenyi Xu,&nbsp;Jing Yu,&nbsp;Yuxin Chang,&nbsp;Ruiyuan Niu,&nbsp;Guanglei Zhu,&nbsp;Ning Tao,&nbsp;Jiangang Sun","doi":"10.1016/j.ndteint.2026.103648","DOIUrl":"10.1016/j.ndteint.2026.103648","url":null,"abstract":"<div><div>In this study, a data conversion method between modulated thermal imaging and flash thermal imaging is derived theoretically and demonstrated experimentally. The method allows for modulated data acquired at one frequency to be forwardly converted to a full flash data which can then be backwardly converted to a modulated data at a different frequency. The experimental demonstrations were carried out using a glass fiber reinforced plastic (GFRP) plate sample that contains flat bottom holes located at various depths. From a forward conversion of measured modulated data, the converted flash data was processed for defect detection by using the thermal effusivity tomography method and the results were compared with the corresponding ones obtained from a flash experiment on the same sample. In addition, backward conversions from the converted flash data to new sets of modulated data at various other frequencies were demonstrated and verified. The results show that this data-conversion method can address the detection of subsurface defects within different depths, which will eradicate the blind-frequency problem and eliminate the need for performing multiple tests with different modulation frequencies.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103648"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective damage detection method based on electromechanical impedance and parallel connection of the transducers","authors":"Shishir Kumar Singh ,&nbsp;Paweł H. Malinowski","doi":"10.1016/j.ndteint.2026.103667","DOIUrl":"10.1016/j.ndteint.2026.103667","url":null,"abstract":"<div><div>Several research studies aim to employ the electromechanical impedance method (EMI) for effective health monitoring. At the same time, limited studies focused on increasing damage detection efficiency using a combination of sensors under noise and temperature variation. This novel research aims to outperform the temperature compensation algorithm development by using a robust multiple-sensor instrumentational strategy for damage detection in structures. This research combines EMI resistance data in parallel connection for damage detection in the steel beam structure. The resistance parameters based on parallel combinations are studied and compared with the output of single transducers or series connections for the added simulated mass, and simulated cracks with variations of the temperature conditions. The performance comparison has been made in the selected frequency range of 1–100 kHz for the additional mass and 30-80 kHz for the simulated cracked steel beam. The damage sensitivity-based performance comparison has been studied using the root mean square deviation (RMSD) index. The resistance data fusion-based parallel connection has shown a better performance of damage detection capability over a single actuator or a series of connected actuators in varying environmental temperature conditions for the real crack and simulated added mass. The simulated added mass and crack are successfully detected at a higher temperature in the case of the parallel combination of the actuators.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103667"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invariants in Eddy Current Testing via dimensional analysis","authors":"Vincenzo Mottola ,&nbsp;Alessandro Sardellitti ,&nbsp;Filippo Milano ,&nbsp;Luigi Ferrigno ,&nbsp;Marco Laracca ,&nbsp;Antonello Tamburrino","doi":"10.1016/j.ndteint.2026.103661","DOIUrl":"10.1016/j.ndteint.2026.103661","url":null,"abstract":"<div><div>The Buckingham’s π theorem has been recently introduced in the context of Non destructive Testing &amp; Evaluation (NdT&amp;E) , giving a theoretical basis for developing simple but effective methods for multi-parameter estimation via <em>dimensional analysis</em>. Dimensional groups, or π-groups, allow for the reduction of the number of parameters affecting the dimensionless measured quantities.</div><div>In many real-world applications, the main interest is in estimating only a subset of the variables affecting the measurements. An example is estimating the thickness and electrical conductivity of a plate from Eddy Current Testing data, regardless of the lift-off of the probe, which may be either uncertain and/or variable. Alternatively, one may seek to estimate thickness and lift-off while neglecting the influence of the electrical conductivity, or to estimate the electrical conductivity and the lift-off, neglecting the thickness.</div><div>This is where the concept of invariants becomes crucial. An invariant transformation is a mathematical mapping or a specific operating condition that makes the measured signal independent of one or more of these uncertain parameters. Invariant transformations provide a way to isolate useful signals from uncertain ones, improving the accuracy and reliability of the NdT results.</div><div>The main contribution of this paper is a systematic method to derive <em>invariant</em> transformations for frequency domain Eddy Current Testing data, via dimensional analysis. The proposed method is compatible with real-time and in-line operations.</div><div>After its theoretical foundation is introduced, the method is validated by means of experimental data, with reference to configurations consisting of plates with different thicknesses, electrical conductivity, and lift-off. The experimental validation proves the effectiveness of the method in achieving excellent accuracy on a wide range of parameters of interest.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103661"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated pixel-level detection method of weld defects using lightweight network on X-ray images","authors":"Weichao Qian ,&nbsp;Shaohua Dong ,&nbsp;Lin Chen","doi":"10.1016/j.ndteint.2026.103672","DOIUrl":"10.1016/j.ndteint.2026.103672","url":null,"abstract":"<div><div>The failure caused by pipeline weld defects is one of the most important pipeline safety accident origins. Due to the impact of the welding process and the environment, weld defects are difficult to avoid. Weld defects are commonly identified by manually inspecting X-ray images, which is inefficient and highly subjective. This paper proposed a pixel-level automatic detection method based on the lightweight encoder-decoder network structure to address the challenges of manual inspection. First, the information loss due to downsampling was reduced by introducing high-low level feature fusion blocks (HLF Block). Then, a new global-local feature fusion block (GLF Block) was established to extract more discriminative features about the defective area, improving the defect detection accuracy. Furthermore, depthwise separable convolution (DSC) was applied to the model to significantly decrease the model parameters without sacrificing detection accuracy. Qualitative comparison and quantitative analysis showed that this technique outperformed competing methods, with a dice coefficient (<em>Dice</em>) of 0.905 and an intersection over union (<em>IoU</em>) of 0.830. Therefore, the proposed inspection model displays significant potential for weld defect detection.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103672"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autoencoder enhanced Bayesian fusion method for damage imaging of composite materials under variable temperature using ultrasonic guided waves","authors":"Ranting Cui ,&nbsp;Guangxing Cai ,&nbsp;Chaojun Wei ,&nbsp;Wei Shen","doi":"10.1016/j.ndteint.2026.103670","DOIUrl":"10.1016/j.ndteint.2026.103670","url":null,"abstract":"<div><div>Carbon Fiber Reinforced Polymer (CFRP) structures are prone to damage under variable temperature environments. Temperature fluctuations not only accelerate damage evolution but also adversely affect guided wave–based techniques commonly used for CFRP damage detection. To address this issue, this study proposes an Autoencoder (AE) -based temperature compensation method combined with a Bayesian fusion framework using ultrasonic guided wave data. The goal is to mitigate the effects of environmental temperature fluctuations and enhance the accuracy of defect localization. The approach is proposed by training the AE with baseline signals collected under a subset of temperature conditions and then reconstructing baseline signals for other temperature by processing damage signals. Experimental validation shows that, with baseline data from only 39 temperature points, the proposed method can accurately reconstruct baseline signals at an additional 117 temperature points. Subsequently, wavelet transform is employed to extract the Time of Flight (TOF) of scattered signals, and a Bayesian data fusion framework is utilized to integrate the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) method with TOF information for precise defect localization. The reconstructed baselines closely align with actual measured baselines, confirming the efficacy of the proposed temperature compensation strategy. In two representative damage scenarios, the proposed Bayesian fusion method reduces the average localization errors by 38.52% and 26.43%, respectively, compared with the conventional RAPID method, while significantly suppressing imaging artifacts.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103670"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature-dependent grain size prediction model for Nimonic 80A superalloy based on multi-feature information of laser ultrasonic and Bayesian neural network","authors":"Yu Peng ,&nbsp;Xiaokai Wang ,&nbsp;Shutong Dai ,&nbsp;Kangwen Huang ,&nbsp;Chaoshan Ren ,&nbsp;Yan Zeng ,&nbsp;Baoming Li ,&nbsp;Rui Zuo ,&nbsp;Xiaochun Gu ,&nbsp;Zhao Liu ,&nbsp;Xianglin Zhang ,&nbsp;Hao Yang","doi":"10.1016/j.ndteint.2026.103673","DOIUrl":"10.1016/j.ndteint.2026.103673","url":null,"abstract":"<div><div>Nimonic 80A superalloy is an important material for key components in aerospace and other fields. Its grain size directly affects high-temperature performance, and achieving grain size detection at different temperatures is crucial for ensuring product quality. Laser ultrasonic technology is characterized by its non-contact nature, making it highly suitable for microstructural characterization of hot metal materials. Bayesian neural networks (BNNs) can handle complex nonlinear relationships and provide quantified prediction results with confidence levels. This study combines laser ultrasonic technology with BNNs. Specifically, the research was conducted as follows: Nimonic 80A superalloy samples with different grain sizes were prepared through rolling deformation and solution treatment. In situ laser ultrasonic detection experiments were conducted on heated samples to obtain signal data from superalloy materials with varying grain sizes ranging from room temperature to 1000 °C. These experiments revealed the effects of temperature and grain size variations on ultrasonic energy attenuation. Ultrasonic attenuation coefficients were calculated based on the amplitudes of laser ultrasonic time-domain and frequency-domain signals. Using temperature and attenuation coefficients as inputs, a grain size prediction method based on BNN was proposed. For comparison, Gaussian Process Regression (GPR) and Quantile Random Forest (QRF) models were also constructed. The three types of models were trained and tested using signal samples, and the performance of these models was compared, demonstrating the applicability and superiority of the BNN model. The results showed that within the sample grain size range of 45.75 μm to 141.38 μm, the maximum prediction error of the BNN model is +7.29 μm (compared with metallographic measurements), and its prediction accuracy is superior to that of the GPR and QRF models. In addition, the predicted values of Nimonic 80A alloy grain size grades were provided in accordance with the industrial standard for grain size grades.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103673"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Damage detection in reinforced concrete slabs of underground chambers using impulse response testing and statistical feature recognition","authors":"Aly Almasry ,&nbsp;Sikandar H. Sajid ,&nbsp;Luc Chouinard ,&nbsp;Jessy Frech-Baronet","doi":"10.1016/j.ndteint.2026.103668","DOIUrl":"10.1016/j.ndteint.2026.103668","url":null,"abstract":"<div><div>Accurate condition assessment is crucial for infrastructure safety, operational reliability, and cost-effective maintenance. This task is particularly challenging for buried structures, given their limited accessibility and complex boundary conditions. This study introduces a rapid non-destructive testing (NDT) methodology based on Impulse-Response test (IRT), enhanced with statistical feature selection and probabilistic analysis, enabling efficient diagnostics with a minimal number of symmetrically placed test points. A reinforced concrete (RC) slab extracted from a decommissioned underground utility chamber was evaluated using IRT at four symmetric locations. Frequency Response Functions (FRFs) were generated for each test point, and Dynamic Time Warping (DTW) was applied to quantify deviations from pairwise FRF comparisons. Statistical dispersion among DTW distances was further characterized using the Gini coefficient (G). The distances were then standardized into Z-scores to derive probabilities reflecting the likelihood of relative local damage. Points with the highest deviation exhibited defect probabilities exceeding 70%, which were subsequently confirmed by ultrasonic shear-wave tomography. To examine robustness, a finite element (FE) model of the slab was developed and validated, then used for parametric analyses of shallow (60 mm) and deep (240 mm) delaminations, both with and without a 150 mm viscoelastic asphalt overlay. Analyses were performed by progressively reducing defect stiffness and mass density to 1%, 5%, 10%, 30%, and 50% of intact properties. Across all scenarios, the framework preserved stable localization and consistent ranking, with defect probabilities reaching up to 81%, demonstrating sensitivity to defect depth and severity as well as resilience to surface damping effects. These results indicate that the proposed baseline-free, symmetry-based framework provides reliable localization of subsurface anomalies in RC slabs, including buried and asphalt-covered configurations.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103668"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved time integrated energy method for imaging extended defect in multilayer composites","authors":"Kang An,&nbsp;Changyou Li","doi":"10.1016/j.ndteint.2026.103644","DOIUrl":"10.1016/j.ndteint.2026.103644","url":null,"abstract":"<div><div>Microwave time reversal based on time integrated energy method (TIEM) was used for the detection of extended defects through combining time-resolved information from multiple sources. However, the wrong localization problem caused by the strong reflection from metal still appears when it is applied for non-destructive testing of multilayer composites backed by metal. In this paper, an improved TIEM (ITIEM) is proposed by properly combining TIEM and the time constraint information obtained from target initial reflection method to overcome the wrong localization problem and ensure the correct localization. Then, microwave time reversal with multiple sources based on ITIEM (ITIEM-MS-MTR) is proposed for the detection of extended cracks with different shapes, such as “V”-shaped crack and “W”-shaped crack. Its effectiveness and noise tolerance is proved through multiple investigations in two-dimensional cases. Furthermore, the proposed ITIEM-MS-MTR is investigated in the detection of extended defect in the multilayer composite skin of an aircraft wing model, and its effectiveness and noise tolerance is finally validated in 2D and 3D cases.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103644"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared thermography coupled with deep learning for fast and reliable predictive monitoring of lubricating oils in dual-use heavy-duty vehicles","authors":"Alicia Ortiz-Chiliquinga ,&nbsp;Fernando Moreno-Haya ,&nbsp;Carlos López-Pingarrón ,&nbsp;Carlos Jesús Vega-Vera ,&nbsp;José S. Torrecilla","doi":"10.1016/j.ndteint.2026.103638","DOIUrl":"10.1016/j.ndteint.2026.103638","url":null,"abstract":"<div><div>The reliable evaluation of lubricating oil condition is critical for ensuring the safety and operational efficiency of heavy-duty equipment in both civilian and defense sectors. Conventional laboratory-based physicochemical analyses, although effective, are inherently time-consuming and do not enable real-time diagnostics or on-site decision-making. In this work, we introduce an innovative approach that leverages infrared thermography coupled with deep learning to achieve rapid, non-destructive, and fully automated classification of lubricating oil samples as either “compliant” (fit for use) or “non-compliant” (unfit for use). The study focuses on two reference lubricants (O-1178 (5W30), gearbox oil and O-1236 (15W40), engine oil) widely deployed in military vehicles, with ground-truth class labels established via standardized laboratory protocols. A comprehensive dataset of over 10,000 thermographic images was generated through controlled cooling cycles, providing the foundation for model development. After comparative analysis of several state-of-the-art convolutional neural network architectures, ResNet-34 and ResNet-50 were selected for their superior performance. The models, trained and validated on stratified and balanced datasets, consistently achieved classification accuracies above 99 %, with the ResNet-34 model delivering 100 % sensitivity and specificity for the detection of non-compliant samples in both oil types. Complementary metrics, including ROC/AUC (≈1.0) and F1-scores near unity, together with stable training–validation loss convergence, confirmed that the classifiers operated in a saturated performance regime with robust generalization. Interpretation with Grad-CAM heatmaps revealed that the model's decisions are grounded in physically meaningful thermal micropatterns directly linked to lubricant degradation. This strategy not only minimizes unnecessary oil changes and associated environmental impact, but also elevates predictive maintenance capabilities by enabling immediate, reliable diagnostics in dual-use (civil and military) settings. The proposed methodology establishes a robust and versatile framework for advanced lubricant condition monitoring, readily adaptable to other industrial fluids and diverse operational scenarios requiring rapid, on-site assessment. Future work will extend this framework to additional lubricant types and broader real-world conditions to further consolidate these findings.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103638"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}