Russian Journal of Nondestructive Testing最新文献_第10页

Detecting and Evaluating Water Ingress in Horizontally Oriented Aviation Honeycomb Panels by Using Automated Thermal Nondestructive Testing 利用自动热无损检测技术检测和评估水平定向航空蜂窝板的进水情况

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI: 10.1134/S1061830923600946

A. O. Chulkov, B. I. Shagdyrov, V. P. Vavilov, D. Yu. Kladov, V. I. Stasevskiy

引用次数: 0

Experience in Developing and Using Metal Detectors for Medical Purposes 开发和使用医疗用金属探测器的经验

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI: 10.1134/S1061830923700602

Yu. Ya. Reutov, V. I. Pudov

引用次数: 0

Method for Identifying the Grout Defects of the Anchors at Ultra-Early-Stage Based on Time-Domain Waveform Characteristic Reflection Points 基于时域波形特征反射点的超早期锚固件灌浆缺陷识别方法

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI: 10.1134/S106183092360079X

Bing Sun, Cong Zhu, Junhui Zou, Shanyong Wang, Sheng Zeng

{"title":"Method for Identifying the Grout Defects of the Anchors at Ultra-Early-Stage Based on Time-Domain Waveform Characteristic Reflection Points","authors":"Bing Sun, Cong Zhu, Junhui Zou, Shanyong Wang, Sheng Zeng","doi":"10.1134/S106183092360079X","DOIUrl":"10.1134/S106183092360079X","url":null,"abstract":"Anchors constitute a common form of structural support in geotechnical engineering. Precise identification of ultra-early-stage (UES) anchoring quality is crucial to ensure the integrity of the secondary lining. To address grout defects in the UES of anchors, a calculation method for UES anchor wave velocity was introduced. Indoor experiments and numerical simulations were conducted for non-destructive testing (NDT) of anchors in the UES, analyzing time-domain waveform characteristics and wave velocity variations. A method for identifying grout defects in the UES of anchors was proposed. The results indicate that the proposed wave velocity calculation method offers a more precise estimation of UES wave velocity for anchors compared to the traditional approach. This enhancement enables a more precise evaluation of the development of solid phases in the anchoring medium. As the solid phase develops, the wave velocity and first wave amplitude of the anchor gradually decline, while the response time of the bottom reflection increases. Grout defects lead to amplified amplitudes in both time-domain and frequency-domain signals, accompanied by a heightened occurrence of peaks in the frequency domain. The waveform distortion region before the bottom reflection is caused by grout defects. In the time-domain signals of defective anchors, a waveform distortion region is observed before the bottom reflection. By assessing the magnitude of the absolute value of the ratio between the amplitude of characteristic reflection points within the distortion region and the amplitude of the first wave, effective identification of grout defects in the UES of anchors can be accomplished.","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"59 12","pages":"1223 - 1240"},"PeriodicalIF":0.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Quantification of Metal Defects in Storage Tanks Based on Machine Learning 基于机器学习的储罐金属缺陷智能量化技术

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI: 10.1134/S1061830923600685

Chao Ding, Yuanyuan He, Donglin Tang, Yamei Li, Pingjie Wang, Yunliang Zhao, Sheng Rao, Chao Qin

{"title":"Intelligent Quantification of Metal Defects in Storage Tanks Based on Machine Learning","authors":"Chao Ding, Yuanyuan He, Donglin Tang, Yamei Li, Pingjie Wang, Yunliang Zhao, Sheng Rao, Chao Qin","doi":"10.1134/S1061830923600685","DOIUrl":"10.1134/S1061830923600685","url":null,"abstract":"Wall-climbing robot are seeing increasing adoption to automated remote and in situ inspection of industrial assets, removing the need for hazardous manned access. The ultrasonic dry-coupling detection device installed on the wall-climbing robot detects the defects of the tank wall. Aiming at the difficulty that the ultrasonic A-scan signal obtained by the ultrasonic dry-coupling detection method has waveform cross-aliasing, which makes it difficult to obtain effective information in traditional feature extraction, Herein, we combine the fast Fourier transform, wavelet packet decomposition and empirical mode decomposition techniques to propose a 3D-SFE method performs multi-scale feature extraction on dry coupled signals. At the same time, in view of the difficulty that traditional nondestructive testing models cannot quantify the defect area accurately, we introduce the XGBoost model to better quantify the defect area. Our proposed defect area quantification model based on multi-scale feature extraction achieves 99.9% accuracy on the training set and 81.5% on the test set. Furthermore, we also analyzed the influence of defect characteristics, sample number, defect shape and depth on the model, and then provided certain guiding significance for the detection of tank defects.","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"59 12","pages":"1207 - 1222"},"PeriodicalIF":0.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Damage Assessment of CFRP-Composite through BILSTM and Hilbert Upper Envelope Analysis 通过 BILSTM 和希尔伯特上包络分析推进 CFRP 复合材料的损伤评估

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI: 10.1134/S106183092360082X

M. Frik, T. Benkedjouh, A. Bouzar Essaidi, F. Boumediene

{"title":"Advancing Damage Assessment of CFRP-Composite through BILSTM and Hilbert Upper Envelope Analysis","authors":"M. Frik, T. Benkedjouh, A. Bouzar Essaidi, F. Boumediene","doi":"10.1134/S106183092360082X","DOIUrl":"10.1134/S106183092360082X","url":null,"abstract":"The aerospace and automotive sectors widely use carbon fiber reinforced plastic because of its exceptional properties, including its high specific modulus, strength, and resistance to fatigue. However, defects such as cracks in the matrix, separation of layers, and separation from bonding can occur during manufacturing and low-velocity impacts, often remaining undetected. As these defects worsen over time, they can significantly weaken the material. To reduce the risk of major failures, regular assessments of carbon fiber reinforced plastic structures are crucial. This study introduces a structural health monitoring technique that minimizes human involvement while effectively tracking the growth of damage in carbon fiber reinforced plastic structures. The approach employs the acoustic emission method and the hilbert transform technique to identify and quantify the progression of damage in carbon fiber reinforced plastic materials. Experimental outcomes from a fatigue test conducted on cross-ply laminates are presented. To precisely predict damage and evaluate the condition of the composite specimen, researchers use the bidirectional long short-term memory model alongside envelope analysis for forecasting. The suggested method achieves a root mean square error of less than 0.03, proving its capability to precisely predict damage and evaluate the condition of the Composite structure. This novel deep learning-driven method adeptly captures the deterioration in performance of carbon fiber reinforced plastic, enhancing predictive accuracy.","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"59 12","pages":"1241 - 1258"},"PeriodicalIF":0.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing 基于超声波、涡流和目视无损检测方法联合诊断结果的管道表面缺陷分类和尺寸确定

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-02-20 DOI: 10.1134/S1061830923601022

N. V. Krysko, S. V. Skrynnikov, N. A. Shchipakov, D. M. Kozlov, A. G. Kusyy

{"title":"Classification and Sizing of Surface Defects in Pipelines Based on the Results of Combined Diagnostics by Ultrasonic, Eddy Current, and Visual Inspection Methods of Nondestructive Testing","authors":"N. V. Krysko, S. V. Skrynnikov, N. A. Shchipakov, D. M. Kozlov, A. G. Kusyy","doi":"10.1134/S1061830923601022","DOIUrl":"10.1134/S1061830923601022","url":null,"abstract":"The issues of classification and characterization of surface operational defects according to the results of ultrasonic, eddy current, and visual inspection methods of nondestructive testing are considered. At the same time, the visual inspection method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented that was applied to classify the images obtained from the TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics in which the obtained models are applied, and determines the accuracy of this algorithm in the RMSE metric, which was calculated within the studied test dataset and amounted to 0.011 mm.","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"59 12","pages":"1315 - 1323"},"PeriodicalIF":0.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Tensile Deformation of 304 Steel Plate Using Electromagnetic Ultrasonic Lamb Waves Mixing 利用电磁超声λ波混合法评估 304 钢板的拉伸变形

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-01-12 DOI: 10.1134/S1061830923600454

Jilun Liu, Suzhen Liu, Liang Jin, Zhichao Cai, Chuang Zhang, Qingxin Yang

引用次数: 0

Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers 用于纤维增强聚合物表层下缺陷检测的四次调频热波成像中的特征识别技术

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-01-12 DOI: 10.1134/S1061830923600788

Naga Prasanthi Yerneni, V. S. Ghali, G. T. Vesala

引用次数: 0

A New Look at the Controllability of Parts with Complex Configuration in Penetrant Testing 渗透测试中复杂结构部件可控性的新视角

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-01-12 DOI: 10.1134/S1061830923600922

I. I. Kudinov, A. N. Golovkov, V. V. Vakhov, D. S. Skorobogatko, A. S. Generalov

引用次数: 0

Time-Frequency Based Thermal Imaging: An Effective Tool for Quantitative Analysis 基于时间频率的热成像：定量分析的有效工具

IF 0.9 4区材料科学

Russian Journal of Nondestructive Testing Pub Date : 2024-01-12 DOI: 10.1134/S1061830923600752

G. V. P. Chandra Sekhar Yadav, V. S. Ghali, S. K. Subhani

{"title":"Time-Frequency Based Thermal Imaging: An Effective Tool for Quantitative Analysis","authors":"G. V. P. Chandra Sekhar Yadav, V. S. Ghali, S. K. Subhani","doi":"10.1134/S1061830923600752","DOIUrl":"10.1134/S1061830923600752","url":null,"abstract":"Recent achievements in TWDAR (thermal wave detection and ranging) technology has made it possible to utilize a range of thermal imaging techniques for analyzing the characteristics of materials used in various industries. Moreover, the distinctive features of nonstationary thermal imaging have piqued attention of researchers in non-destructive evaluation (NDE). For a detailed defect visualization, it is essential to employ a dependable processing technique that accurately extracts the relevant time–frequency components from the chirped thermal response. In this study, a nonstationary thermal wave imaging technique is utilized by using quadratic frequency modulation (QFM) in conjunction with a cutting-edge technique of fractional Fourier transform (FrFT), to assess material quality. An experimentation has been carried out on carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) samples with defects of different sizes at varying depths, to evaluate their characteristics. Experimental results have validated the efficiency of the proposed FrFT processing approach through rigorous qualitative and quantitative analysis, which has involved measurements of some merit figures, such as signal-to-noise ratio (SNR), full width at half maxima (FWHM), and probability of detection (PoD). From the results, it is evident that the proposed method provides a distinct and precise visualization of defects promising to be a useful technique in identifying and retrieving information of internal defects in materials.","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"59 11","pages":"1165 - 1176"},"PeriodicalIF":0.9,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139460457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0