Journal of Nondestructive Evaluation最新文献

{"title":"Particle Filter-Based Fault Prognosis of Live XLPE Insulated Aerial Bundled Cables Installed at Coastal Regions Using Historical Infrared Thermography Data","authors":"Moez ul Hassan,&nbsp;Tariq Mairaj Rasool Khan,&nbsp;Waleed Bin Yousuf,&nbsp;Aqueel Shah","doi":"10.1007/s10921-024-01152-y","DOIUrl":"10.1007/s10921-024-01152-y","url":null,"abstract":"<div><p>Accelerated ageing of cross-linked polyethylene (XLPE) insulated aerial bundled cables (ABC) installed at coastal regions for electric power distribution is a topic of major concern. Sudden failures caused by rapid insulation deterioration initiate unexpected power shutdowns. Accurate prognosis of incipient failures in cables will enable maintenance agencies to plan repair or replacement activities well on time thus improving the reliability and availability of the transmission grid. In this research work, historical non-destructive evaluation (NDE) data was generated using infrared thermography of ABCs to ascertain cable degradation parameter. Historical NDE data embodies the progressive degradation experienced by the ABCs installed at harsh marine environment. The degradation growth in cable insulation, when subjected to rough environmental conditions, is non-linear coupled with non-Gaussian / multimodal noise distributions. Therefore, a renowned nonlinear Bayesian estimator namely Particle Filter (PF) is applied on the historical database to determine degradation growth evolution over time. The proposed framework is further complimented with f-step prediction scheme in future time to quantify the prediction accuracy of cable degradation growth in future where measurement data is not available. The so predicted results are then compared to the actual degradation in future. The prediction accuracy demonstrates the efficacy of proposed technique for prognosis of ABCs installed at different locations. Accurate state prediction in different life phases of ABCs further displays robustness of proposed technique in estimating actual degradation growth during the different life stages.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01152-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370014","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":"An End-to-End Quantitative Identification Method for Mining Wire Rope Damage Based on Time Series Classification and Deep Learning","authors":"Chun Zhao,&nbsp;Jie Tian,&nbsp;Hongyao Wang,&nbsp;Zhangwen Shi,&nbsp;Xingjun Wang,&nbsp;Jingwen Huang,&nbsp;Lingguo Tang","doi":"10.1007/s10921-025-01166-0","DOIUrl":"10.1007/s10921-025-01166-0","url":null,"abstract":"<div><p>Mining wire rope (MWR) is an important part of mine hoisting equipment and plays a key role in mining operations. Damage to these ropes can significantly reduce production efficiency and pose serious safety risks to workers. Therefore, quantitatively identifying damage in MWR is of great importance. Traditional methods for damage signal identification rely on manual feature extraction (MFE), which depends heavily on experience and lacks stability and flexibility. This paper proposes an end-to-end (E2E) quantitative identification model for MWR damage based on time series classification (TSC) and deep learning (DL). Unlike traditional methods, the E2E model learns features directly from the one-dimensional raw signals of MWR damage and does not require MFE. In order to test its validity and versatility, experiments were conducted on three different datasets. The results show that the E2E method performs well in quantitatively identifying MWR damage compared to other methods and this method meets the requirements of the mining industry in terms of precision and efficiency to ensure safe and reliable operation of mining work.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369987","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":"Weakly Supervised Complex Texture Defect Detection Based on Nested U-Net Architecture","authors":"Kuidong Huang,&nbsp;Yang Zeng,&nbsp;Julong Zhao,&nbsp;Shijie Chai,&nbsp;Fuqiang Yang","doi":"10.1007/s10921-025-01161-5","DOIUrl":"10.1007/s10921-025-01161-5","url":null,"abstract":"<div><p>The rich and complex texture information of industrial products, and the fact that the number of normal products often far exceeds the number of defective products in industrial scenarios, poses a great challenge to product quality inspection. In order to solve this challenge, a weakly supervised defect detection model based on nested U-Net was proposed: the nested U-Net was used as the main body of the model, and an attention module was introduced, which could obtain the relationship between local features and between feature channels. Furthermore, A weakly supervised training strategy was employed: the defect mask from the Berlin noise, external data and normal samples are used to synthesize the defects, and a few real defect samples are randomly inserted into the synthetic defect samples to train the detection model. Experimental validation was carried out on the public datasets MVTec AD, DAGM, MT and custom CT (computed tomography) composite material dataset, and the evaluation indicators included image-level AUC (area under the receiver’s operating characteristic curve), pixel-level AUC and AP (average accuracy). Experimental results show that the proposed method achieves excellent performance of 99.9%/98.7%/84.1%, 99.1%/95.3%/76.1%, 100%/98.1%/86.7% and 73.6%/69.1%/36.0% on three types of metrics on four datasets, respectively, which is better than the current advanced model.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370049","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":"CNN-Based Similar Microwave Reflection Signals for Improved Detectability and Intelligent Characterization of Internal Defects in Composite Materials","authors":"Mingyu Gao,&nbsp;Liang Huo,&nbsp;Fei Wang,&nbsp;Peng Song,&nbsp;Yulong Gao,&nbsp;Guohui Yang,&nbsp;Junyan Liu,&nbsp;Zhipeng Liang,&nbsp;Yunji Xie,&nbsp;Yinghao Song","doi":"10.1007/s10921-025-01163-3","DOIUrl":"10.1007/s10921-025-01163-3","url":null,"abstract":"<div><p>Near-field microwave imaging shows considerable promise for non-destructive evaluation of internal defects in high silica/phenolic composites, which are commonly used as thermal protection systems (TPS) for rocket/missile solid motor nozzles and space re-entry vehicles. However, effectively identifying defect features using post-processing algorithms remains challenging. To address this challenge, this paper proposes a microwave defect characterization algorithm based on Convolutional Neural Networks (CNN). A defect dataset derived from reflection microwave signals, was manually compiled by detecting samples with critical defects. The CNN framework was utilized for precise classification of microwave signals, employing a classification encoding strategy to extract two-dimensional defect information and achieve automatic localization and imaging of defects. Multiple deep learning models were compared in both simulations and experiments, revealing that the proposed CNN exhibited significant advantages in feature extraction, enabling highly effective identification of internal defects even with a limited dataset. Compared with traditional algorithms, the detection accuracy of the proposed 1D-SENet has been improved by 53.35% and 50.66%, respectively, and can achieve detection of defects with a minimum size of Φ6mm. These validate the effectiveness of algorithm in intelligent and automated microwave characterization of delamination defects within composite materials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370019","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":"High Quality Ultrasonic Imaging at Low Detection Frequency for Defects in Thick Composites","authors":"Hui Zhang,&nbsp;Min Zhang,&nbsp;Haiyan Zhang,&nbsp;Yiting Chen,&nbsp;Wenfa Zhu,&nbsp;Qi Zhu","doi":"10.1007/s10921-024-01155-9","DOIUrl":"10.1007/s10921-024-01155-9","url":null,"abstract":"<div><p>Improving detection accuracy without reducing detection depth has always been a challenge in ultrasound imaging. For multi-layer anisotropic carbon fiber reinforced polymers (CFRPs), the propagation path of ultrasonic waves becomes complex with severe attenuation. To improve the imaging accuracy of defects in CFRPs, a beam multiply and sum method suitable for full matrix data, FM-BMAS, has been proposed. This method introduces the spatial coherence of ultrasonic array signals through cross-multiplication operation. A new harmonic component is generated and high-quality imaging of side-drilled holes (SDHs) is achieved without affecting the detection depth. The FM-BMAS method can detect two SDHs with a diameter of 1 mm at a depth of 8 mm at 2.5 MHz detection frequency. In contrast, these defects cannot be visualized by the classical total focusing method due to ultrasonic attenuation even with 5 MHz detection frequency. Furthermore, FM-BMAS can achieve higher image resolution and superior noise suppression capabilities in CFRPs compared to other methods.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361638","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":"Spreading and Shrinking Effects of Coplanar Capacitive Sensors for Surface Defects","authors":"M. Mwelango,&nbsp;X. Yin,&nbsp;M. Zhao,&nbsp;R. Fan,&nbsp;Z. Han,&nbsp;G. Fan,&nbsp;P. Ma,&nbsp;X. Yuan,&nbsp;W. Li","doi":"10.1007/s10921-025-01160-6","DOIUrl":"10.1007/s10921-025-01160-6","url":null,"abstract":"<div><p>With increasing attention to safety, accurate detection of defects in diverse materials is crucial. Non-Destructive Evaluation (NDE) techniques play a key role in this effort. However, many approaches overlook preliminary signal analysis, which is vital for understanding fundamental sensor signal characteristics and improving advanced techniques. This study aims to investigate the fundamental signal properties from coplanar capacitive sensors (CCS) with square electrodes to gain insights into the relationship between signal properties and actual linear defect size in both conducting and non-conducting materials. The first-order derivatives (1st O.DE) of raw signals obtained from CCS through both simulations and experiments were further analysed, focusing on a circular surface defect. Nine sensor configurations were tested to examine the spreading effect. Experiment and simulation results were in good agreement, showing that the Full Width at Zero (FWZ) of the raw signals of both materials is greater than the actual defect diameter (spreading effect) whereas the signals processed using the 1st O.DE exhibited peak-trough widths greater than the actual defect diameter in non-conducting materials (spreading effect) and less than the actual diameter in conducting materials (shrinking effect). These results underscore that the spreading and shrinking effects are intrinsic characteristics of the CCS, attributed to the behavior of the CCS’s electric field and sensitivity distribution field (SDF) when interacting with different materials. By incorporating these insights into novel and advanced methods—such as imaging algorithms, machine learning approaches, and data fusion techniques—future developments can be effectively guided to enhance the accuracy, reliability, and advancement of defect detection, imaging, and sizing in coplanar capacitive sensing for NDE.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361639","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":"Effect of a Crack on the Displacement Current Field of Non–electrically Conductive Materials via Electromagnetic Induction Testing","authors":"Wataru Matsunaga,&nbsp;Yoshihiro Mizutani","doi":"10.1007/s10921-024-01151-z","DOIUrl":"10.1007/s10921-024-01151-z","url":null,"abstract":"<div><p>The range of application of eddy current testing (ECT) has been recently extended to non-electrically conductive materials, in which case it is called electromagnetic induction testing (EIT) given that EIT detects changes in the electromagnetic field of the displacement current. Although EIT has been reported for non-destructive characterization of non-electrically conductive materials, its detection principle is still unclear. We used finite element analysis (FEA) and experiments to evaluate the effect of cracks on the displacement current field in non-electrically conductive materials for crack detection using EIT. FEA was performed on electrically and non-electrically conductive materials with slits simulating cracks. The FEA results showed that crack detection differed between materials, as eddy currents bypassed the crack, while displacement currents passed through and formed a current path. Furthermore, the electric field intensity and displacement current induced in the non-electrically conductive materials varied significantly in the cracked area compared with the uncracked area. Experiments were conducted to detect cracks in carbon fiber reinforced thermoplastics (CFRTPs) and glass fiber reinforced plastics (GFRPs), which have isotropic electrical properties in the in-plane direction. In the CFRTP, the electromagnetic field varied significantly even at locations far from the crack, whereas it changed only slightly near the crack in the GFRP. This result demonstrates that for non-electrically conductive materials, EIT can identify cracks by detecting localized changes in the displacement current flowing through the cracks. Our findings can help clarify the principle of crack detection in non-electrically conductive materials, thereby extending the application of EIT to these materials.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184612","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":"Developing a Neural Network Based Microwave Sensing System for Accurate Salinity Prediction in Water","authors":"Muhammed Ismail Pence,&nbsp;Cemanur Aydinalp,&nbsp;Semih Doğu,&nbsp;Mehmet Nuri Akıncı","doi":"10.1007/s10921-024-01156-8","DOIUrl":"10.1007/s10921-024-01156-8","url":null,"abstract":"<div><p>High and low salinity levels play a crucial role in the vitality of organisms and affect natural ecosystems, agricultural yields and human health. To mitigate the risks associated with high blood pressure and cardiovascular diseases, the World Health Organization (WHO) advocates reducing salt consumption among adults, suggesting an intake of no more than 5 g daily. In this study, a non-invasive microwave (MW) sensing approach, that is augmented by deep neural network (DNN)  models is proposed to predict salinity levels. The MW detection measurement system, including a Horn antenna, has been developed to evaluate the salt content in bottled spring waters (BSWs). The system with DNN  model provides a novel solution for real-time water quality monitoring. The input and output dataset for DNN  model were generated using four different BSWs, each with a salt content ranging from 0 to 32 g and increased by 1 g. The developed DNN  model, designed with six fully connected layers, uses reflection coefficients (RCs) as input dataset to predict salt content in grams accurately. The accuracy performance of the DNN  model in various bandwidths was evaluated by dividing the 1–13 GHz range into 78 different bands and the lowest error rate was found to be in the 1–8 GHz bandwidth (2.18%). Furthermore, each BSW was measured five times, and the performance of the model was evaluated according to the number of measurements. In three or more measurements, the model demonstrated notable improvement(15.3%) in predicting salt content.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-024-01156-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107896","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 Inductive Coupling Analysis (EMICA): A New Tool for Imaging Internal Defects in Carbon Fiber Composites","authors":"Kevin Finch,&nbsp;David C. Long,&nbsp;Taylor Ott,&nbsp;Bradley Spatafore,&nbsp;Joshua R. Biller","doi":"10.1007/s10921-025-01157-1","DOIUrl":"10.1007/s10921-025-01157-1","url":null,"abstract":"<div><p>Carbon fiber laminates enjoy a wide range of applications from innovative architectural design to aerospace and the safety overwrap for pressure vessels. In the case of carbon fiber overwrapped pressure vessels (COPVs), the overwrap thickness can vary from 6 mm (∼ 0.25 inch) for thin-walled COPV up to 25 mm (∼ 1”) or more for thick walled COPV, depending on the vessel type. The failure mechanisms for carbon fiber are more complex than for metals and monitoring COPVs for defects or fatigue over their lifetime is further complicated by the thickness of the carbon fiber used. Traditional electromagnetic NDE methods, such as eddy current testing (ECT) for imaging defects in these structures has been severely limited, achieving accurate identification to about 4 mm in depth. In this paper, a new technique is introduced to address these shortcomings, Electro-Magnetic-Inductive-Coupling-Analysis, or EMICA, can be used to detect damage inside thick carbon fiber laminate pieces. EMICA is based on the interaction of the repeating three-dimensional structure of carbon fiber and low-frequency electromagnetic waves that are allowed to actively spread through the conductive bulk composite material highlighting defects such as delamination and fiber disruptions, well below the laminate surface. In this paper, EMICA is demonstrated in flat carbon fiber laminates up to ∼ 12 mm (0.5”) thick, made in-house, with known defects hidden through the thickness of the piece that cannot be detected via visual inspection. Delaminations, cuts/cracks, and the underlying ply layup structure can all be identified in the EMICA images. It is shown that three imbedded PTFE delaminations at varying depths (3 mm, 6 mm, 9 mm) are simultaneously imaged using EMICA in a ½” thick CF laminate [0°/90°] panel with an excitation frequency of 40 kHz. Furthermore, the electromagnetic focal point can be chosen within the depth of CF composites by intelligently selecting the excitation frequency for the ply layup being probed, while the traditional penetration depth equation does not hold true in these complex structures.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995371","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":"Bedrock Identification and Bedrock Depth Prediction in Asphalt Pavements Using Pavement System Transfer Function","authors":"Qi Sun,&nbsp;Yanqing Zhao,&nbsp;Yujing Wang,&nbsp;Ruoyu Wang,&nbsp;Bosen Li","doi":"10.1007/s10921-025-01159-z","DOIUrl":"10.1007/s10921-025-01159-z","url":null,"abstract":"<div><p>To determine optimal road maintenance and repair schedules, road agencies need to regularly evaluate asphalt pavement performance during both construction and operation. It usually involves back-calculating the pavement’s deflection responses to obtain modulus for each structural layer. However, bedrock under the subgrade can significantly affect this analysis. To enhance the accuracy of back-calculation, this study proposed bedrock depth prediction models based on pavement system transfer function (PSTF) aided by falling weight deflectometer (FWD) tests. To provide sufficient data for model development, a spectral element method with fixed-end boundary conditions (B-SEM) was used to calculate the deflection responses of various pavement structures under different bedrock conditions. Based on the transfer function (TF) theory of linear time-invariant (LTI) systems, Fourier transform (FT) was used to process time-domain data, resulting in the PSTF for each pavement structure, which was then used as the dataset. This study also analyzed the amplitude spectrum characteristics of PSTFs under different bedrock depths and proposed methods for identifying bedrock under the subgrade. A bedrock depth prediction model (PSTF-BD) based on the PSTF was developed using the results of the sensitivity analysis. The model’s performance was comprehensively evaluated using various error metrics. The results indicate that the PSTF-BD model demonstrates high accuracy in predicting bedrock depth. Specifically, the PSTF-BD (B) model achieves a correlation coefficient of 99.6%, with an average error of no more than 1.0% for the prediction results of the validated dataset. Compared to existing prediction models, the PSTF-BD model improves correlation by at least 6.4% and prediction accuracy by at least 34.1%. Furthermore, the PSTF-BD model offers superior predictive performance and is well-suited for engineering applications, showcasing significant potential for widespread adoption in road engineering projects.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995113","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}