Automated Eddy Current System for Aircraft Structure Inspection

Lysenko, Iuliia; Kuts, Yurii; Petryk, Valentyn; Malko, Volodymyr; Melnyk, Andrii

doi:10.2478/tar-2023-0021

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The complex interplay of factors, including array size, geometry, material properties, and inspection parameters, demands comprehensive evaluation [7,10]. This study addresses challenges identified in previous research by providing experimental insights into optimizing ECA performance for enhanced defect detection in aircraft components [7,11,12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects

Lysenko,

Kuts,

Uchanin

et al. 2024

Transactions on Aerospace Research

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Eddy 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.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects

Lysenko,

Kuts,

Uchanin

et al. 2024

Transactions on Aerospace Research

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Digital Twins for Condition and Fleet Monitoring of Aircraft: Toward More-Intelligent Electrified Aviation Systems

Sadeghi,

Bellavista,

Song

et al. 2024

IEEE Access

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The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as "a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections". In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector.

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Defect Reconstruction Using Multilayer Perceptron for Regression and Classification Tasks Based On Eddy Current Signatures

Barrarat,

Helifa,

Lefkaier

et al. 2024

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The safety of engineering structures can be significantly compromised by cracks resulting from manufacturing procedures or prior loading, potentially leading to severe and catastrophic industrial accidents. Therefore, it is crucial to accurately and quantitatively characterize cracks in such structures. One common technique for detecting defects in metallic structures is eddy current testing (ECT). This paper proposes a method for the reliable estimation of crack shape and dimensions in conductive materials using the principles of ECT combined with a machine learning algorithm. First, numerical simulations are used to examine the relationship between the detection signature and the crack length and depth. Then, an artificial neural network based on a machine learning technique is employed to inversely characterize the cracks. The predicted results demonstrate that the crack length, depth, and shape can be accurately determined by the proposed algorithm. These findings, obtained using various specimens with known cracks, validate the applicability of the proposed approach for crack characterization.

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Automated Eddy Current System for Aircraft Structure Inspection

Cited by 3 publications

References 12 publications

Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects

Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects

Digital Twins for Condition and Fleet Monitoring of Aircraft: Toward More-Intelligent Electrified Aviation Systems

Defect Reconstruction Using Multilayer Perceptron for Regression and Classification Tasks Based On Eddy Current Signatures

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