Russian Journal of Nondestructive Testing最新文献

{"title":"Mapping of the Grain Size Number in Cylindrical Specimens from Images Reconstructed by the Digital Aperture Focusing Method","authors":"A. E. Bazulin,&nbsp;E. G. Bazulin,&nbsp;A. H. Vopilkin,&nbsp;S. V. Romashkin,&nbsp;D. S. Tikhonov","doi":"10.1134/S1061830926601716","DOIUrl":"10.1134/S1061830926601716","url":null,"abstract":"<p>In the process of manufacturing various blanks, it becomes necessary to estimate the grain size (crystallites), which determines the mechanical properties of the material. Traditional metallographic studies are laborious and make it possible to estimate the grain size only on the surface of the workpiece, but not inside it. The dependence of ultrasound scattering on grain size and frequency makes it possible to estimate grain size when analyzing ultrasound attenuation or when estimating the amplitude of scattered pulses. The digital aperture focusing (DFA) method restores high-resolution images of reflectors in the form of scattered field amplitude maps. By reconstructing images from echo signals measured by phased arrays with operating frequencies of 2.25, 5, and 10 MHz and using Rose’s theory, it is possible to estimate the grain size inside the test object.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"903 - 911"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176379","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}

{"title":"Research on Magnetic Anisotropy Detection Method and System Based on Time-Frequency Analysis Enhancement","authors":"Liting Wang,&nbsp;Libo Feng,&nbsp;Zunyang Hu,&nbsp;Jianfu Li,&nbsp;Chongyin Liu,&nbsp;Nan Meng","doi":"10.1134/S1061830926600292","DOIUrl":"10.1134/S1061830926600292","url":null,"abstract":"<p>To achieve high-precision detection of magnetic anisotropy in ferromagnetic materials, this paper develops a magnetic anisotropy detection system based on enhanced time–frequency analysis. The hardware platform mainly consists of an excitation board, a power amplifier, a data acquisition card, sensors, and a host PC. The software is developed on the LabVIEW platform and integrates data acquisition, real-time signal processing, and feature analysis into a unified measurement and control system. Through frequency-sweep and amplitude-sweep experiments, the optimal operating parameter range of the sensor is determined, and the system repeatability is evaluated using the coefficient of variation of the measured data. The results demonstrate that the system exhibits good repeatability for 21 characteristic parameters, satisfying the requirements for high-precision detection. On this basis, the short-time Fourier transform is introduced into the anisotropy analysis of magnetic Barkhausen noise, and a multidimensional feature parameter system is established. Experimental comparisons between 30QG130 grain-oriented silicon steel and X70 pipeline steel indicate that the proposed time–frequency domain method exhibits higher sensitivity in quantifying the degree of magnetic anisotropy and identifying the rolling direction of materials than conventional time-domain methods. The anisotropy coefficient <i>k</i> reaches its maximum value in the critical magnetization region. Further experimental results show that different anisotropic mechanisms exhibit distinct response patterns in the time and frequency domains. Time–frequency analysis can directly reveal the magnetic domain magnetization dynamics by simultaneously observing time-domain evolution and frequency-domain energy distribution, thereby providing a novel and effective approach for decoupling complex magnetic anisotropy coupling mechanisms.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"968 - 982"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176553","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}

{"title":"Time Frequency Domain Analysis of Internal Diseases in Asphalt Pavement Based on Ground Penetrating Radar","authors":"Weizhen Liu,&nbsp;Xin Yu,&nbsp;Yuyang Feng","doi":"10.1134/S1061830926600590","DOIUrl":"10.1134/S1061830926600590","url":null,"abstract":"<p>Ground penetrating radar (GPR), often referred to as the “CT” of road, can be applied to conduct a comprehensive analysis of the internal structure and potential defects of roads, thereby providing an accurate assessment of its overall condition. However, current qualitative analysis of the collected radar data primarily relies on mapping, which limits the ability to effectively assess the varying degrees of damage caused by the underlying defects. In this paper, gprMax is used to model the forward simulation of GPR signals through asphalt pavement, focusing on cracks, interlayer defects, loose structures. The study explores the time-frequency characteristics of varying degrees of damage caused by these defects, aiming to analyze how the time-frequency eigenvalues change as the severity of the damage increases. A sensitivity analysis is conducted to examine the sensitivity of different time-frequency eigenvalues to variations in the degree of damage. The non-stationary characteristics of signals along both the time and frequency axes are analyzed using wavelet transform. Finally, the results of the forward simulation are validated against the measured data. The results show that five time-domain eigenvalues and four frequency-domain eigenvalues are effective in comprehensively characterizing varying degrees of damage caused by three types of defects: cracks, interlayer defects, and loose structures. Specifically, the trend of change in crack eigenvalues is opposite to that of interlayer defects and loose structures. Among the time-frequency eigenvalues, the square difference (SQD) and quadratic variance (QV) in the time domain, as well as the mean value (MV) and standard deviation (STD) in the frequency domain, exhibit greater sensitivity to the degree of road damage. As the degree of damage increases, the shifts in peak frequency for three defects differ. The peak frequencies of cracks, at 1.4 and 2.0 GHz, shift to higher frequencies, while the peak frequency of interlayer defects, at 2.0 GHz, shifts to lower frequencies. In contrast, the peak frequency of loose structures, at 1.4 GHz, shifts to higher frequencies. In summary, time-frequency characteristics offer an effective approach to analyzing varying degrees of damage caused by the internal road defects, thereby providing theoretical support for the advancement of ground penetrating radar detection from qualitative image recognition to quantitative analysis.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"995 - 1015"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176557","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}

{"title":"Improving 3D-GPR Interpretation of Subsurface Distresses in Asphalt Pavements Using a Random Aggregate Forward Model and Field Validation","authors":"Yanping Gan,&nbsp;Xiaohua Li,&nbsp;Xiaodong Xie,&nbsp;Jie Gao,&nbsp;Liang Song,&nbsp;Jiangang Yang","doi":"10.1134/S1061830926600905","DOIUrl":"10.1134/S1061830926600905","url":null,"abstract":"<p>Three-dimensional ground-penetrating radar (3D-GPR) is increasingly used in pavement engineering for rapid nondestructive detection of subsurface distresses, but waveform interpretation is hindered by background scattering caused by the heterogeneous meso-structure of asphalt mixtures. This study develops a pavement-engineering-oriented GPR forward-modeling framework in which aggregate gradation and particle geometry are represented explicitly rather than simplified as a homogeneous background. Based on the gradation of AC-20 and AC-25 mixtures and measured electromagnetic parameters, a random aggregate pavement model was established in gprMax using the finite-difference time-domain (FDTD) method, and a two-dimensional profile-oriented framework was adopted to generate B-scan responses directly comparable with field 3D-GPR longitudinal profiles. A homogeneous model was analyzed in parallel to illustrate the qualitative difference in scattering background introduced by the explicit representation of aggregates. Typical concealed distresses, including reflective cracks, concealed cracks, interlayer debonding, interlayer voiding and loosening, were then simulated to establish a B-scan response atlas. The engineering applicability was evaluated using 3D-GPR data acquired along six expressway sections totaling 6.23 km; a representative 1.8-km subsection was further verified by milling and coring, in which 109 semi-rigid-base reflective cracks, 108 interlayer-debonding areas and 39 concealed cracks were identified and cross-checked. The results show that the random aggregate model reproduces a more realistic scattering background than the homogeneous model, while the main response features of different distress types remain distinguishable. The developed atlas shows clear correspondence with field anomalous waveforms and verified damage locations, supporting more reliable distress identification, localization and maintenance decision-making in asphalt pavements.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"1016 - 1040"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176480","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}

{"title":"Investigation into Laser Ultrasonic-Based Disc Spring Preload Detection Technology","authors":"Ze Tian,&nbsp;Ming Zhang,&nbsp;Jianfeng Ye,&nbsp;Tao Song,&nbsp;Jun Zhang","doi":"10.1134/S106183092660022X","DOIUrl":"10.1134/S106183092660022X","url":null,"abstract":"<p>As a core component of the operating mechanism of high-voltage circuit breakers, the disc spring plays a crucial role in maintaining the preload of the tension bolt under complex loads, thereby ensuring the safe operation of high-voltage circuit breakers is essential to preserving the tensioning bolts’ preload under complicated loads and guaranteeing the circuit breakers’ safe functioning. As a result, precisely determining the disc spring’s preload has significant theoretical and practical implications. In this study, a laser ultrasonic approach for non-contact disc spring preload force sensing is presented. The detecting location distribution of laser ultrasonic was clarified by using a numerical analytical approach to examine the disc spring’s stress and deformation distribution under various preload forces. Using a numerical analysis approach, the laser-excited surface wave’s propagation properties in the disc spring material were examined. The findings demonstrate the importance of the disc spring material’s acoustic elastic impact. A laser ultrasonic detection platform comprising a pulsed laser emitter, galvanometer, vibrometer, detection software, etc., was constructed in order to detect the disc spring’s preload force. The detection platform’s in-situ loading device was used to finish calibrating the disc spring laser ultrasonic detection system. The self-developed laser ultrasonic detection equipment was used to conduct the tests. The findings indicate that this system can meet the criteria of engineering applications with a relative inaccuracy of 9.93% for the disc spring’s preload force.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"944 - 955"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176555","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}

{"title":"Verification of the Methodology for Optimization of Shape Restoration of Surface Corrosion Defects","authors":"L. V. Mikhailov,&nbsp;A. V. Mikhailov,&nbsp;E. Yu. Sazhina,&nbsp;A. V. Nikitin,&nbsp;Ya. G. Smorodinskii,&nbsp;V. N. Kostin","doi":"10.1134/S1061830926601649","DOIUrl":"10.1134/S1061830926601649","url":null,"abstract":"<p>This paper presents a method for reconstructing the geometry of corrosion defects in ferromagnetic objects using magnetic nondestructive testing data based on leakage field (MFL) recording. The approach is formulated as a solution to an inverse magnetostatic problem in an optimization formulation. The defect geometry is parameterized using a B-spline surface, which allows for the description of complex shapes with a limited number of parameters. The locations of control points and defect boundaries are determined by analyzing the distributions of magnetic field components and their derivatives. The optimization criterion is the standard deviation between experimental and calculated data, which is minimized using a gradient-free algorithm. Numerical modeling of the forward problem is implemented using the finite element method. The effectiveness of the method is confirmed through numerical and field experiments for defects of various shapes. It is shown that while integral characteristics, such as defect volume, are reconstructed with high accuracy, local parameters (maximum depth and shape) are reconstructed with greater error. The influence of defect geometry complexity on solution accuracy was determined. The obtained results demonstrate the potential of the proposed approach for quantitative defect assessment based on MFL inspection data.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"956 - 967"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176479","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}

{"title":"Detectability of Discontinuities in Welded Joints Using Nondestructive Testing Methods for Nuclear Power Plant Equipment","authors":"A. D. Zhukov,&nbsp;V. M. Ushakov,&nbsp;V. I. Surin,&nbsp;N. V. Meleshko,&nbsp;A. R. Z. Ras El Oued","doi":"10.1134/S1061830926601674","DOIUrl":"10.1134/S1061830926601674","url":null,"abstract":"<p>The results of non-destructive testing of a 30 mm thick specimen containing an extended discontinuity 150 mm in length are presented. The following methods were used: classical pulse-echo with A-scan, antenna arrays based on the A1550 IntroVisor ultrasonic flaw detector, radiographic, and electrical (contact potentiometry method). The echo method with discontinuity assessment according to the standards applicable in nuclear energy was adopted as the baseline. A comparison of the discontinuity assessment using the above methods is carried out. The advantages and disadvantages of the synthetic aperture focusing technique (SAFT) and the contact potentiometry method are shown, which are proposed to be used as optional testing for welded joints of nuclear power facilities.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"1063 - 1073"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176481","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}

{"title":"An Enhanced Solar Panel Crack Detection System Using Resource Optimized Depthwise Convolutional Feature Incorporated Vision Transformer Model","authors":"C. Sivaranjani,&nbsp;V. Ravi","doi":"10.1134/S1061830926600255","DOIUrl":"10.1134/S1061830926600255","url":null,"abstract":"<p>The defects in solar panels reduce the solar energy production and hence the detection of these defects in solar panels is important. The timely intervention and inspection of the cells in the solar panels reduce the losses in the solar panels energy production process. This can be done by the automated solar panel inspection method. This research work proposes a fully automated Artificial Intelligence (AI) based solar panel crack detection system using Convolutional Neural Network (CNN) feature incorporated with Vision Transformer Model (VTM). The main objective of this work is to classify the drone captured solar panel images into either cracked or non-cracked. This proposed work is designed with the Anisotropic Diffusion (AD) filter, Multi-Window Gabor Transform (MWGT), Resource Optimized Depthwise Convolutional Neural Networks (RODCNN) architecture and Vision Transformer Model (VTM). AD filter is used to smooth the regions while suppressing noises in images. The MWGT enhances the time-frequency resolution property of each pixel in the AD filtered image and then a novel RODCNN architecture is designed for computing the solar panel features. The features are further classified using the VTM classification approach, which classifies the solar panel images into either cracked or non-cracked. This proposed methodology is tested and validated on two solar panel imaging datasets RoboFlow and Kaggle.The average computational crack detection results have been attained as 98.84% SPSe, 99% SPSp, 98.94% SPA, 98.97% SPPR, 99.07% FSM, and 99.07% MCC in RoboFlow dataset. The average computational crack detection results have been attained as 98.93% SPSe, 99.01% SPSp, 98.96% SPA, 98.98% SPPR, 98.99% FSM, and 98.91% MCC in Kaggle dataset. The key values of the experimentations of the proposed methodology are compared with the recent state-of-the-art methods.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"1041 - 1062"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176554","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}

{"title":"Application of Multichannel Acoustic Measurements for Diagnostics of Rolling Bearing Failures","authors":"V. V. Ershov,&nbsp;I. V. Khramtsov,&nbsp;M. M. Shobei,&nbsp;I. A. Sudakov,&nbsp;S. V. Panin,&nbsp;K. A. Denisov","doi":"10.1134/S1061830926601650","DOIUrl":"10.1134/S1061830926601650","url":null,"abstract":"<p>The study is devoted to the application of a multichannel acoustic radiation measurement method for rolling bearing fault diagnosis. The applicability of the spectral kurtosis diagram for detecting the evolution of transients excited by faulty (damaged) bearings was assessed. A series of acoustic measurements of noise parameters was carried out on a test rig using a microphone array under free-field and reverberant field conditions. An analysis of the envelope spectra calculated for the acoustic emission of a rolling bearing with an inner ring defect shows good agreement between the experimentally measured and calculated values of the characteristic frequencies. This is a reliable indicator of the presence of this type of defect in this particular structural element of the bearing. The method of acoustic visualization of noise sources implemented on the test rig using a microphone array demonstrates the possibility of confidently identifying a faulty bearing relative to a healthy one when mounted on a single shaft of a laboratory installation, even in complex acoustic conditions.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"891 - 902"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176482","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}

{"title":"Research on Damage Identification of Carbon Fiber Honeycomb Panels Based on Guided Waves and Correlation Entropy Spectra in Strong Noise Environments","authors":"Liandong Xie,&nbsp;Chenhui Su,&nbsp;Xiaomei Zhao,&nbsp;Xiudong Li,&nbsp;Mingshun Jiang,&nbsp;Faye Zhang","doi":"10.1134/S1061830926600012","DOIUrl":"10.1134/S1061830926600012","url":null,"abstract":"<p>Carbon fiber honeycomb panels have the advantages of lightweight, high strength, and thermal insulation, and are widely used in manufacturing industries such as aerospace and high-speed rail. However, some hidden damages are inevitable during service, which may cause serious consequences, especially in strong noise environments where damage identification is particularly difficult. This article focuses on the above issues and conducts research on damage identification of carbon fiber honeycomb panel structures through simulation and experiments. Firstly, a simulation model of carbon fiber honeycomb panel is constructed using ABAQUS finite element software, sensors are arranged and structural response signals are collected. Based on the relevant entropy spectral density, damage factors are extracted to achieve damage identification of carbon fiber honeycomb structures; Secondly, experimental research was conducted by arranging sensor arrays on carbon fiber honeycomb panels to collect structural response signals. The correlation entropy spectral density method was employed to extract damage factors, ultimately achieving damage localization for carbon fiber honeycomb structures. Under noise-free conditions, the absolute damage localization errors for simulation and experiments were 4.4 and 0.22 mm, respectively; under Gaussian white noise conditions, the errors were 6.22 and 0.14 mm; and under impulse noise conditions, the errors were 7.81 and 0.22 mm. This provides new technical means and theoretical support for health monitoring of honeycomb sandwich structures, and has important theoretical significance and application value.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"62 7","pages":"1074 - 1096"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176552","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}