Journal of Nondestructive Evaluation最新文献

{"title":"Demonstrating the Viability of Through-Transmission Eddy Current for Crack Detection","authors":"Catalin Mandache,&nbsp;Antal Prigli","doi":"10.1007/s10921-026-01328-8","DOIUrl":"10.1007/s10921-026-01328-8","url":null,"abstract":"<div><p>In eddy current testing, a time-varying magnetic field generated by a current-carrying coil is coupled with an electrically conductive specimen. The interaction of the induced electromagnetic field with the part, specifically with its material properties and geometrical discontinuities, causes a change in the impedance of the same (transmit or driver) coil or a separate (receive or sense) coil. When a transmit-receive type probe is used, either with two concentric or side-by-side coils, both coils are placed on the same side of the part. The receiving coil couples with the material under test, but even more with the driving coil; therefore, this arrangement reduces discontinuity sensing capabilities of the technique. To interrogate over the full thickness of the part, it is proposed herein to emulate the through-transmission ultrasonic testing for eddy current inspections, with the driver and the receiver coils on opposite sides of the investigated part. A stationary, planar, large-diameter printed-circuit copper coil is mounted on the far side of the part or embedded in a multi-layer structure, while a pencil-type probe is employed to scan in a plane parallel to the part’s top surface. Either of the coils could be used for driving or sensing. and the coupling between the two coils is realized through the entire thickness of the evaluated part. With the planar coil as a driver and the pencil coil as a receiver, the depth of investigation is enhanced by the area of the excitation coil, while the resolution is improved by the scanning step and small diameter of the sensing probe. The work is a demonstration of the viability of through-transmission eddy current testing with structure embedded printed circuit board coils for in-situ condition monitoring of metallic structures.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01328-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147336283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electromagnetic Transmission Characteristics of Some Concrete Materials Commonly Used in Türkiye at Lower Microwave Frequencies","authors":"Şevket Demirci,&nbsp;Hakan Işıker,&nbsp;Betül Yılmaz,&nbsp;Caner Özdemir","doi":"10.1007/s10921-026-01329-7","DOIUrl":"10.1007/s10921-026-01329-7","url":null,"abstract":"<div><p>This study aims to identify the electromagnetic wave conduction capability of steel-fiber reinforced C-30/37 concrete and autoclaved aerated (or foamed) concrete, which are commonly used in Türkiye, for the <span>(400)</span> to <span>(2400)</span> MHz band to make provisions for penetration depth at these frequency ranges. Transmission-only <span>({text{S}}_{21})</span> measurement results in free-space are presented for different material thicknesses. Multipath propagation effects are mitigated using an accurate gating scheme in the time domain. Two characterization techniques are proposed: direct attenuation estimation from the measured data and complex permittivity estimation using low-loss dielectric approximation. Both techniques yield almost identical attenuation results, showing a significant variation over the studied frequency band. Results indicate that signal attenuation ranges from <span>(26)</span> dB/m at <span>(400)</span> MHz to <span>(66)</span> dB/m at <span>(2400)</span> MHz for C-30/37 concrete whereas these numbers are measured as <span>(33)</span> dB/m at <span>(400)</span> MHz and <span>(58)</span> dB/m at <span>(2400)</span> MHz for aerated concrete. Proposed analysis methods can be quantitatively very useful and employed accurately, especially for determining and estimating the dielectric and conductive parameters of any homogeneous material. Based on these findings, the results of power budget analyses and exemplary radar experiments are presented and evaluated to determine the limits of the applicability of detecting and locating a living person behind such materials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-026-01329-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147336282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D-CNN-based Acoustic Recognition Model for Large Wind Turbine Blade Abrasion Faults","authors":"Haoming Mo,&nbsp;Fanyong Yin","doi":"10.1007/s10921-025-01324-4","DOIUrl":"10.1007/s10921-025-01324-4","url":null,"abstract":"<div><p>A 3D-CNN-based acoustic pattern recognition model is developed for accurate detection of abrasion faults in wind turbine blades. The model first processes acoustic vibration signals through empirical mode decomposition and wavelet denoising to account for local signal characteristics. The denoised signals are then subjected to frame splitting, windowing, and discrete Fourier transform to construct two-dimensional energy spectrograms, which are subsequently downscaled using Mel-filter banks to extract distinctive acoustic features associated with blade abrasion faults. These features are input into an innovative three-dimensional convolutional neural network for fault identification. Experimental results demonstrate the model’s effectiveness, achieving a peak recognition accuracy of 98.8% and consistent performance with accuracy rates above 94% across tests. The model exhibits a reliable capability to distinguish between normal operational sounds and various severities of blade abrasion faults, while maintaining low misjudgment rates.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact Acoustic Detection Method of Tile-Wall Bonding Integrity Based on Wavelet Transform and CNN-SVM","authors":"Yuqi Wang,&nbsp;Yang Liu,&nbsp;Yi Zhang,&nbsp;Shu Li,&nbsp;Dongdong Chen","doi":"10.1007/s10921-026-01331-z","DOIUrl":"10.1007/s10921-026-01331-z","url":null,"abstract":"<div><p>The acoustic signal feature extraction and intelligent diagnosis method for tile debonding are of great significance to ensure building safety. This paper presents a detection method based on wavelet transform and Convolutional Neural Network (CNN) integrated with Support Vector Machine (SVM) to solve the problem that the detection results of traditional defect detection methods based on frequency domain characteristics are unstable under environmental noise imbalance. In this study, the acoustic vibration signals polluted by real environmental noise were collected. The time–frequency diagrams were obtained using the complex Morlet wavelet transform, which captured the temporal and spectral variations of the acoustic signals. The image recognition method of CNN was enhanced through the integration of SVM, which replaces the softmax classifier with SVM. Four tile-wall specimens with different degrees of debonding were crafted, and the tiles were divided into nine different regions for tapping. Model training and prediction were conducted on the acoustic signals acquired from identical regions across the four specimens, which verified the reliable classification performance of this method. The average test accuracy of the nine regions reached over 98%, which provides a basis for the study of debonding quantification. Moreover, the traditional CNN was also employed for model analysis, and comparative result revealed that the proposed method demonstrates superiority in accuracy and efficiency. In the future, more groups of experiments on debonding area gradients could be conducted to research whether this method can accurately classify the degree of bonding defects when the obtained data set is large and sufficient.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Damage Identification Method for Wind Turbine Blade Fatigue Testing Based on Acoustic Emission Signals","authors":"Shouxiang Sun,&nbsp;Jinghua Wang,&nbsp;Xingjie Zhang,&nbsp;Leian Zhang","doi":"10.1007/s10921-026-01334-w","DOIUrl":"10.1007/s10921-026-01334-w","url":null,"abstract":"<div><p>Wind turbine blades are prone to various types of damage under long-term fatigue loading, making accurate damage type identification critical for structural health monitoring and operation and maintenance decision-making. This study proposes a hybrid algorithm framework that integrates SOM, Principal Component Analysis (PCA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Random Forest (RF) to identify damage in wind turbine blades based on acoustic emission (AE) signals collected during fatigue testing. Firstly, nonlinear and linear dimensionality reduction is performed on the raw AE features using SOM and PCA, respectively, resulting in six representative feature parameters. Then, DBSCAN is employed to cluster and label the reduced-dimension samples, enabling unsupervised signal classification without requiring prior knowledge. Based on the clustering results, a Random Forest model is trained and evaluated in a supervised manner, with classification accuracy, F1-score, and generalization performance quantitatively assessed. Experimental results show that the proposed method achieves over 90% accuracy in a four-class classification task, significantly outperforming traditional methods in both precision and stability. The clustering process exhibits strong robustness and is suitable for monitoring damage evolution at various stages of fatigue for the blade. This study provides an efficient and scalable signal processing approach for damage identification in composite wind turbine blades, laying a methodological foundation for intelligent and automated AE-based monitoring systems.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Defect Detection of Steel Plates by Electromagnetic Tomography Imaging Based on Edge-DeepLabv3+","authors":"Xianglong Liu,&nbsp;Kun Zhang,&nbsp;Ying Wang,&nbsp;Huilin Feng,&nbsp;Nan Wang","doi":"10.1007/s10921-026-01330-0","DOIUrl":"10.1007/s10921-026-01330-0","url":null,"abstract":"<div><p>Electromagnetic tomography (EMT) is a promising nondestructive imaging technique for metallic defect detection. However, its inherent soft-field diffusion, rapid sensitivity decay, and nonlinear eddy-current interaction lead to severe boundary blurring and low-resolution reconstructions, particularly for small or multiple defects. To address these limitations, this study proposes Edge-DeepLabv3+, a physics-informed deep reconstruction network specifically designed for metallic EMT. The model integrates SE-Res2Net for multi-scale conductivity encoding, DenseASPP for dense receptive-field expansion under strong diffusion, ESA for capturing long-range electromagnetic correlations, and a Boundary-Refinement (BR) decoder to compensate for soft-field-induced edge loss. Furthermore, we introduce an Edge-Focused Hybrid Loss (EHL), which combines global MSE, imbalance-aware Dice loss, and a boundary-supervision BCE applied to morphology-derived defect contours, enabling precise recovery of high-frequency conductivity discontinuities. A physics-based dataset comprising 12,960 samples is generated using COMSOL, incorporating coil misalignment, temperature drift, non-white noise, and mutual-coupling perturbations through domain randomization, ensuring robustness against practical domain shifts. Extensive experiments on both simulation and real EMT systems demonstrate that Edge-DeepLabv3+ significantly improves reconstruction accuracy, boundary fidelity, and robustness to noise compared with LBP, Tikhonov, SE-Res2Net, and DeepLabv3+. The proposed model achieves accurate reconstruction of 3–6 mm single and multiple defects, even under low-SNR (10 dB) conditions, highlighting its strong potential for reliable online metallic defect monitoring in industrial environments.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSFF-YOLO: A Multi-Scale Feature Fusion Network for Aero-Engine Blades Surface Defects Detection","authors":"Jiaqi Yuan,&nbsp;Lixiang Zhao,&nbsp;Wenguang Ye,&nbsp;Yunyong Cheng,&nbsp;Wenfeng Cai","doi":"10.1007/s10921-025-01321-7","DOIUrl":"10.1007/s10921-025-01321-7","url":null,"abstract":"<div><p>Reliable detection of aero-engine blade surface defects is hindered by weak defect saliency, long-tailed category imbalance, and strong geometric priors from curved surfaces. This paper proposes MSFF-YOLO, a multi-scale feature fusion framework built upon YOLOv11. The method integrates a Multi-scale Efficient Aggregation Module (MEAM) for enhancing subtle and edge-attached defects, a multi-scale FMSIoU loss for improving regression robustness under long-tailed distributions, and a Manhattan Self-Attention (MaSA) mechanism for modeling curvature-related spatial dependencies. Evaluated on the high-resolution AeBSDD dataset, MSFF-YOLO achieves an <i>mAP₅₀</i> of 89.1%, surpassing YOLOv11 especially on nick, bent, and dent defects. Real-world illumination-disturbance tests and zero-shot evaluation on NEU-DET further verify its strong cross-scene and cross-domain generalization, demonstrating its robustness for industrial blade inspection.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LightDefectNet-18: A Lightweight Framework for Multi-Domain Defect Detection","authors":"Ajantha Vijayakumar,&nbsp;Joseph Abraham Sundar Koilraj,&nbsp;Muthaiah Rajappa,&nbsp;Ramakrishnan Sundaram","doi":"10.1007/s10921-025-01327-1","DOIUrl":"10.1007/s10921-025-01327-1","url":null,"abstract":"<div><p>Detection of defects in diverse domains requires a specialized object detection system capable of identifying various types of flaws of different sizes and shapes. This research addresses detection challenges across six critical domains: saline bottle level monitoring, screw defect detection, magnetic tile inspection, road crack analysis, fabric flaw identification, and potato leaf disease recognition. These applications exhibit unique visual characteristics, including variable defect morphologies, subtle texture variations, and domain-specific features, which conventional detection models often fail to adequately address. Standard Faster R-CNN implementations with ResNet-50 and VGG-16 backbones offer general feature extraction but lack domain-specific optimization for these specialized applications. We propose LightDefectNet-18, a custom CNN backbone for Faster R-CNN featuring dual residual blocks with skip connections, strategic kernel sizing, and progressive channel expansion, integrated with a Feature Pyramid Network (FPN) architecture. The FPN component creates a multi-scale feature hierarchy through top-down pathways and lateral connections, effectively detecting defects across scales. The architecture incorporates batch normalization layers, calibrated dropout, and proper weight initialization to enhance feature preservation and gradient flow. When integrated with Faster R-CNN, we implement refined anchor configurations optimized for multi-scale defect detection across our target applications, with tailored anchor sizes and aspect ratios for each pyramid level. The detection pipeline employs an adaptive optimization strategy with learning rate scheduling and early stopping mechanisms. The quantitative evaluation demonstrates superior detection performance across all target applications compared to standard backbones, with significant improvements in Average Precision using a relaxed IoU threshold specifically calibrated for industrial defect detection scenarios. The model's FPN-enhanced architecture effectively addresses the challenges of capturing fine-grained visual features essential for distinguishing subtle anomalies at multiple scales in specialized materials while maintaining computational efficiency suitable for deployment in real-world industrial and agricultural monitoring systems, even with limited training data.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic Emission as a Stochastic Microfailure Process: Unified Quantitative Laws","authors":"David Sánchez-Molina,&nbsp;Silvia García-Vilana","doi":"10.1007/s10921-025-01317-3","DOIUrl":"10.1007/s10921-025-01317-3","url":null,"abstract":"<div><p>The prediction of fracture and progressive damage in solid materials can be assessed with acoustic emission (AE) monitoring, yet most quantitative AE-stress/strain relationships remain largely empirical. In this work, we present a unified stochastic framework that derives and generalizes these relationships from a microfailure distribution. By modeling the temporal occurrence of microfailures as a stochastic Poisson process, we establish direct statistical connections between AE activity and macroscopic mechanical variables such as stress and strain. This perspective allows us to reinterpret proposed empirical AE laws as particular statistical regimes, while also advancing a generalized formulation capable of capturing more complex behaviors often observed under dynamic loading. Extensive stochastic simulations further reveal that simple assumptions about internal deterioration rates naturally lead to heuristic quantitative laws for the number of AE events, thereby grounding empirical observations in probabilistic reasoning. The framework is validated against experimental datasets from cortical bone and collagenous soft tissue, confirming its robustness and predictive capacity. Beyond providing a rigorous theoretical foundation for empirical AE laws, our results demonstrate how microlevel statistical assumptions can explain macroscopic fracture signatures, offering new tools for structural health monitoring and prediction of fractures.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influences of Surface Properties on the Reflection Intensity - Towards in Situ Monitoring During Early Age Hydration of CEM I","authors":"Eric Schönsee,&nbsp;Götz Hüsken,&nbsp;Amarteja Kocherla,&nbsp;Christoph Strangfeld","doi":"10.1007/s10921-025-01326-2","DOIUrl":"10.1007/s10921-025-01326-2","url":null,"abstract":"<p>Interlayer bonding in 3D concrete printing is influenced by the hydration progress and surface moisture of the previously printed layer. For effective quality control, continuous in situ monitoring of interlayer surface properties is required. This study investigated reflection intensity as a method for in situ measurements during the hydration of CEM I mixtures with varying retarder contents. Additional factors influencing the reflection intensity are also examined. Two laser line scanners with different wavelengths were used to track hydration over 72 h. Vicat tests and isothermal calorimetry served as reference methods. Across all the mixtures, the reflection intensity exhibited a repeatable pattern with five different stages. A sharp increase in intensity during the third stage was consistent with the acceleration period of hydration. These findings suggest that reflection intensity measurements could serve as a promising tool for evaluating interlayer bonding in 3D concrete printing.</p><p>The material used in this study is a cement lime, based on CEM I 42.5 N, with varying retarder content. From each batch of material, two samples were prepared for Vicat testing, two samples were prepared for isothermal calorimetry measurements, and one sample was cast for monitoring the reflection intensity. Two laser profile scanners were used, operating at 405 nm and 658 nm, respectively. Data were acquired for 72 h. The results show a strong increase in reflection intensity during the acceleration period.</p>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01326-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}