Статті (АСНК)
Постійне посилання зібрання
У зібранні розміщено матеріали, що опубліковані або готуються до публікації в наукових журналах та збірниках.
Переглянути
Перегляд Статті (АСНК) за Назва
Зараз показуємо 1 - 20 з 74
Результатів на сторінці
Налаштування сортування
Документ Відкритий доступ Ac sourcing current frequency influence on eddy-current testing(Институт по механика-БАН, Институт по информационни и комуникационни технологии-БАН, 2014) Lysenko, Iuliia; Zakrevskyi, OleksandrДокумент Відкритий доступ Advantages of Using Eddy Current Array for Detection and Evaluation of Defects in Aviation Components(Bulgarian Society for NDT, 2023) Lysenko, Iuliia; Mirchev, Yordan; Levchenko, Oleksandr; Kuts, Yurii; Uchanin, ValentynThis article examines the potential of Eddy Current Array (ECA) as a non-destructive testing (NDT) technique for aviation components. Experimental investigations were conducted using a specially prepared sample with various defects. The scanning results demonstrated ECA's enhanced detection sensitivity, high inspection speed, and ability to characterize defects. The study emphasizes the need to optimize the inspection process and utilize automated subsystems for signal filtration and result evaluation. The findings support the adoption of ECA as an effective NDT technique in the aviation industry, contributing to improved safety and reliability. This research provides valuable insights for the development of guidelines and standards, enhancing the overall efficiency of aviation inspections.Документ Відкритий доступ Analysis of formation processes of informative features in eddy current probes with pulsed excitation mode(NDT.net, 2023) Lysenko, I.; Kuts, Y.; Uchanin, V.; Protasov, A.The modern pulsed eddy current (EC) technique for flaw detection in structure inspections of aircraft and automotive elements, or other responsible constructions is typically carried out in aperiodic mode. At the same time, the unstable characteristic points of the EC signal usually used as informative parameters can restrict the potential of this excitation mode due to influence measurement errors. The article considers an application of the pulsed EC method of NDT based on the oscillatory mode. To obtain the conditions concerned with different modes of EC probe response oscillations, an equivalent scheme of the “testing object – EC probe” system was developed. The conditions represent the signal formation process and allow analyzing it for impedance and differential probes. The obtained mathematical model of the probe signals allowed for the dependence of proposed signal parameters on the characteristics of the testing object to be evaluated due to simulation. According to this, the frequency and attenuation coefficient of natural oscillations are proposed as the informative parameters. Also, the simulation results are used for developing enhanced algorithmic software for determining and analyzing the EC signals. The proposed informative parameters are experimentally investigated using a set of specimens. The obtained experimental and simulation results are correlated and it confirms the possibility of the proposed methodology to enhance the inspection procedures related to the specimen’s parameters measurements as well as the detected defect sizing.Документ Відкритий доступ Application of YOLOX deep learning model for automated object detection on thermograms(2022-01-26) Skladchykov, Ivan; Momot, Andrii; Galagan, Roman; Bohdan, Halyna; Trotsiuk, KaterynaДокумент Відкритий доступ Automated Eddy Current System for Aircraft Structure Inspection(Sciendo, 2023) Kuts, Yurii; Lysenko, Iuliia; Petryk, Valentyn; Malko, Volodymyr; Melnyk, AndriiAircraft part diagnostics are crucial during both production and maintenance, with eddy current non-destructive testing (ECNDT) being the method of choice due to its cost-effectiveness, informativeness,productivity, and reliability. ECNDT excels regardless of surface condition or coatings. It’s employed fordiagnosing various aircraft components, necessitating diverse transducer types, excitation modes, andadvanced signal processing. To improve ECNDT, this article explores integrating harmonic and impulseexcitation modes in a single tool to enhance informativeness. Building upon a wireless eddy current system,the authors propose a comprehensive method for processing and displaying information suitable for objectcondition monitoring systems. The system includes automated transducer mode control and experimentaldata processing algorithms. The constant expansion of tested objects and new materials underscoresthe need to enhance the theoretical foundations of eddy current non-destructive testing, refine signalprocessing techniques, and identify informative signs. This demands the development of new automatedECNDT tools, and this article offers a promising avenue for improvement. The results include model andexperimental tests of system components, showcasing the potential of this approach to enhance ECNDTeffectiveness, automation, and informativeness in the realm of aircraft part diagnostics.Документ Відкритий доступ Automated Eddy Current System for Express Monitoring(Bulgarian Society for NDT, 2022) Kuts, Yurii; Lysenko, Iuliia; Petryk, Valentyn; Protasov, Anatolii; Alexiev, AlexanderДокумент Відкритий доступ Automated segmentation of ultrasound medical images using the Attention U-Net model(2024) Momot, A.; Zaboluieva, M.; Galagan, R.The article deals with the method of automated semantic segmentation of ultrasound medical images using the Attention U-Net deep learning model. The advantages of using Attention blocks in neural network architectures for segmentation tasks are analyzed. To test the described algorithms, the Breast Ultrasound Images training dataset was chosen. The method described in the article allows for automating the process of detecting and preliminary classification of breast tumors based on the analysis of ultrasound images. As a result of training the Attention U-Net model, the Mean IOU value of 49.2% was obtained on the test set. The network can automatically classify the detected neoplasm as benign or malignant with an F1 Score of 0.87. The results indicate the prospects of using the Attention U-Net model in the tasks of analyzing ultrasound medical images. Ways to further improve the considered method are proposedДокумент Відкритий доступ Automation of ultrasound breast cancer images classification using deep neural networks(Sciences of Europe, 2022) Momot, Andrii; Galagan, Roman; Zaboluieva, MartaДокумент Відкритий доступ Deep learning automated data analysis of security infrared cameras(2021) Momot, Andrii; Skladchykov, IvanДокумент Відкритий доступ Deep Learning Automated System for Thermal Defectometry of Multilayer Materials(2021) Momot, Andrii; Galagan, Roman; Gluhovskii, VictorДокумент Відкритий доступ Determination of Mechanical Properties of Paramagnetic Materials by Multi-frequency Method(BSNDT, 2019) Kalenychenko, Yuriy; Bazhenov, Victor; Kalenychenko, Aleksandr; Koval, Viktor; Ratsebarskiy, SergiyДокумент Відкритий доступ Eddy Current Array Testing of Steel Tube Profiles(Bulgarian Society for NDT, 2023) Mirchev, Yordan; Lysenko, Iuliia; Borisov, Tsvetomir; Kovtun, Vadim; Chukachev, PavelThis paper addresses testing longitudinal welded joint in a thin-walled tube using semi-automatic eddy current test system and flexible eddy current array probe. The system is by OLYMPUS (EVIDENT). These test results from artificially induced discontinuities have been compared and analysed relative to visual test results. The capability of the semi-automated eddy current system to detect discontinuities located on the inner surface of a 3.2 mm thick tube was studied.Документ Відкритий доступ Enhanced Feature Extraction Algorithms Using Oscillatory-Mode Pulsed Eddy Current Techniques for Aircraft Structure Inspection(Institute of Aviation, 2021) Kuts, Yurii; Lysenko, Iuliia; Uchanin, Valentyn; Protasov, Anatolii; Redka, MykhailoДокумент Відкритий доступ Evaluation Of Eddy Current Array Performance In Detecting Aircraft Component Defects(Institute of Aviation, 2024) Lysenko, Iuliia; Kuts, Yurii; Uchanin, Valentyn; Mirchev, Yordan; Levchenko, OleksandrEddy current array (ECA) technology is increasingly being used in the aerospace industry for non-destructive testing of aircraft components. This study evaluates the performance of ECA in detecting defects in aircraft components, focusing on its effectiveness, reliability, and sensitivity. The study evaluates the effectiveness of ECA technology in eddy current defectoscopy by introducing a dimensionless efficiency coefficient, then seeks to validate this coefficient through experimental testing of aircraft component materials with artificially induced defects of various sizes, types, and orientations to simulate real-world scenarios. ECA’s sensitivity in detecting small and subsurface defects is analyzed, along with precise defect sizing and positional information. Reliability and repeatability are investigated through repeated measurements. Furthermore, the article analyses the impact of various factors on the performance of ECA, including surface conditions, probe configurations, and inspection parameters. Comparative analysis is performed to assess the advantages and limitations of ECA in comparison to other conventional inspection methods. The findings of this study will contribute to a better understanding of the capabilities and limitations of ECA in detecting aircraft component defects. The results will aid in optimizing inspection strategies, enhancing the reliability of defect detection, and improving the overall maintenance practices in the aerospace industry.Документ Відкритий доступ Experience in the Use of Surface NDT for the Diagnostics of Military Equipment During Full-scale Military Operations(Bulgarian Society for NDT, 2022) Poddubchenko, A.; Lysenko, I.; Hlabets, S.; Posypaiko, Y.; Pavlyi, O.Документ Відкритий доступ Influence of architecture and training dataset parameters on the neural networks efficiency in thermal nondestructive testing(2019-10) Momot, Andrii; Galagan, RomanDescribes the perspective of the use of artificial neural networks in automated thermal non-destructive testing and defectometry systems. The influence of backpropagation neural networks architecture on the efficiency of defect classification and accuracy of determining their depth and thickness are analyzed. Considered the influence of volume and quality of training dataset on the efficiency of defect classification and accuracy of defectometry. Performance of neural networks is evaluated by quantitative indicators, such as MSE, relative error and Tanimoto criterion. The optimal neural network architecture for using in active thermal testing was established on the basis of experimental researches.Документ Відкритий доступ Intelligent Automated Eddy Current System for Monitoring the Aircraft Structure Condition(IEEE, 2022) Lysenko, Iuliia; Uchanin, Valentyn; Petryk, Valentyn; Kuts, Yurii; Protasov, Anatolii; Alexiev, AlexanderДокумент Відкритий доступ Methodology for Measuring Phase Shifts of Signals Using Discrete Hilbert Transform(Institute of Measurement Science, Slovak Academy of Sciences, 2021-05) Kuts, Yurii; Kochan, Orest; Lysenko, Iuliia; Huminilovych, RuslanaДокумент Відкритий доступ Results of the experimental research of dynamic vibration processes of the rail for rolling stocks fault diagnostics(JVE International Ltd., 2017) Nozhenko, Olena; Kučera, Pavel; Cherniak, Ganna; Pistek, Vaclav; Suslov, Evgeniy; Nozhenko, Volodymyr; Kravchenko, KostiantynДокумент Відкритий доступ Reviewing challenges in the application of eddy current arrays and their impact on NDT efficiency(НАУ, 2023) Lysenko, Iuliia; Kuts, Yurii; Mirchev, Yordan; Levchenko, OleksandrEddy current arrays (ECAs) have gained significant popularity in the field of non-destructive testing (NDT) due to their ability to provide fast and accurate inspection results. However, several challenges associated with using ECAs in NDT need to be overcome in order to improve inspection performance. One of the primary challenges with ECAs is the impact of array size and geometry on inspection effectiveness. The size and shape of the ECA directly influence the sensitivity and resolution of the inspection process, making it challenging to optimize these parameters for specific inspection scenarios. Furthermore, the use of large ECAs can lead to increased noise levels and reduced inspection speed, negatively affecting the overall efficiency of the inspection process.