TECHNICAL APPLICATION OF IMAGE PROCESSING IN THE IDENTIFICATION OF FOREIGN OBJECTS IN THE AEROSPACE INDUSTRY
DOI:
https://doi.org/10.69609/1516-2893.2024.v30.n2.a3924Keywords:
Foreign Object, Aerospace Industry, Infrared, Image ProcessingAbstract
In the aviation industry, foreign objects (FOs) - any items not part of the aerospace product - pose risks of damage if they come into contact with aircraft. This study explores the implementation of both automated and manual image processing techniques for the detection of FOs, aiming to enhance safety by reducing inspection times and improving quality over traditional manual methods. The proposed approach utilizes infrared cameras for image capture in low-light or complex environments, combining automated detection algorithms with manual oversight. Experiments were conducted on the structures and cockpit of the Super Tucano 314, Xavante AT-26, and the F5 (J85 model) engine, employing the Yoosee application for imaging and Python with Anaconda Spider for processing. The results demonstrate the system's ability to identify FOs that are difficult to detect visually, ultimately contributing to a reduction in operational risks within the aerospace sector.
References
BENAMEHD, B. D. et al. Design and realization of an aeronautical cleaning robot for aircraft maintenance 4.0 based on artificial intelligence. Materials Today: Proceedings, 2023. 3521-3526.
CHEN, W. et al. Foreign object debris surveillance network for runway security. Aircraft Engineering and Aerospace Technology: An International Journal, Beijing, v. 83, n. 4, p. 229-234, 2011.
CHICALKSKY, R., KLEINA, C., BASSINELLO, D. G., NOGUEIRA, V. V., JESUS, F. S., & BARROS, L. B. (2024). Guide robot with artificial intelligence for visually impaired People. CUADERNOS DE EDUCACIÓN Y DESARROLLO, 16(3), 01-28.
CHO, S. W. et al. Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network. Sensors, p. 1-31, 2018.
CHOI, S. R. et al. Foreign object damage in flexure bars of two gas-turbine grade silicon nitrides. Materials Science and Engineering, Cleveland, p. 411-419, 2004.
CORKE, P. Robotics, Vision and Control: Fundamental Algorithms in Matlab. Springer Tracts in Advanced Robotics, Springer Science & Business Media, 73, 2011.
EVERS, R.; MARTERS, P. The application of low-altitude near-infrared aerial photography for detecting clandestine burials using a UAV and low-cost unmodified digital camera. Forensic Science International, p. 408-418, 2018.
GURI, M.; BYKHOVSKY, D. aIR-Jumper: Covert air-gap exfiltration/infiltration via security cameras & infrared (IR). Computers & Security, v. 82, p. 15-29, 2019.
HARNISCHMACHER, C. Digital Infrared Photography. [S.l.]: Rocky Nook, 2008.
INTERNATIONAL QUALITY MANAGEMENT SYSTEM. IAQG AS9100 D. Standard for the Aviation, Space and Defense (AS&D) industry. IAQG, 2016.
INTERNATIONAL AEROSPACE QUALITY GROUP. AS9100:2018 – Quality management systems – Requirements for aerospace standards, 2018.
INTERNATIONAL AEROSPACE QUALITY GROUP. AS9146:2022 – Aerospace series – Quality management systems – Risk management, 2022.
KWON, H.-J.; LEE, S.-H. Visible and Near-Infrared Image Acquisition and Fusion for Night Surveillance. Chemosensors, p. 1-16, 2021.
LIU, L. et al. Dynamic Behavior and Damage Mechanism of 3D Braided Composite Fan Blade under Bird Impact. International Journal of Aerospace Engineering, p. 16, 2018.
LIU, Y.; JIN, Z.; TENG, L. PSO-Based Time Optimal Rapid Orientation for Micronano Space Robot. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, v. 59, n. 2, p. 1921-1934, 2023.
LOWE, C. Foreign Object Debris: from dinged props to fatal accidents, an F-16 mishap reveals how FOD can be a hazard for all Aircraft. Aircraft Systems, p. 8-10, February 2015.
Luz, T. D., Sieben, J., Sequeira, J. J., & Amaral, A. E. (Mar de 2024). Desenvolvimento de um sistema de processamento de imagem para detecção de ovos de galinha em linhas de produção. Revista Gestão e Conhecimento, 18(1).
MARANDI, S. M.; RAMANI, K.; TAJDARI, M. Foreign object damage on the leading edge of gas turbine blades. Aerospace Science and Technology, Tehran, n. 33, p. 65-75, 2014.
MELCHIORRI, C. Robot teleoperation. Encyclopedia of Systems and Control, 2014. 1-14.
MELICHAR, M.; SKRIVANOVÁ, N. Foreign Object Damage Issued By Industry 4.0. 31 ST DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, 2020. 0202-0208.
MELICHAR, M.; SKRIVANOVÁ, N. Foreign Object Damage Issued By Industry 4.0. 31 ST DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, 2020. 0202-0208.
NATIONAL AEROSPACE STANDARDS. NAS412:2023 – National Aerospace Standard for Non-Destructive Testing, 2023.
O'DONNELL, M. J. Airport Foreign Object Debris (FOD) Detection Equipment. FAA. Washington DC, p. 14. 2009. (150/5220-24).
O'DONNELL, M. J. Airport Foreign Object Debris (FOD) Management. Federal Aviation Administration (FAA). Washington DC. 2010. (AC 150/5210-24).
OLSEN, T. L.; TOMLIN, B. Industry 4.0: Opportunities and challenges for operations management. Manufacturing & Service Operations Management, 22, 2020. 113-122
ÖZTURK, S.; KUZUCUOĞLU, A. E. A multi-robot coordination approach for autonomous runway Foreign Object Debris (FOD) clearance. Robotics and Autonomous Systems, Gebze, Instambul, v. 75, n. Part B, p. 244-259, 2016.
PATTERSON, J. Foreign Object Debris (FOD) detection research. International Airport Review, Atlantic City NJ, v. 11, n. 2, 2008.
QI, J. et al. Reinforcement learning-based stable jump control method for asteroid-exploration quadruped robots. Aerospace Science and Technology, v. 142, 2023.
RABBITT, J. R. National Transportation Safety Board - Safety Recommendation. NTSB. Washington, D.C., p. 10. 2011. (A-11-7 through -11).
SAE International. SAE AS9146. Foreign Object Damage (FOD) Prevention Program - Requirements for Aviation, Space, and Defense Organizations. SAE, 2022.
TAAFFE, K. M.; ALLEN, R. W.; GRIGG, L. Performance metrics analysis for aircraft maintenance process control. Journal of Quality in Maintenance Engineering, v. 20, n. 2, p. 122-134, 2014.
TABOSA, J. M., OLIVEIRA, A. S., & HAYAMA, A. D. (2024). Neural Networks Applied in the Identification of Soybean Rust. Cuadernos de Educación Y Desarrollo, 16(1), 1978-1993.
XU, Y. et al. Foreign Object Damage Performance and Constitutive Modeling of Titanium Alloy Blade. International Journal of Aerospace Engineering, Shaanxi Province e Heilongjiang Province, v. 2020, 2020.
YAN, X.; SHAN, M.; LINGLING, S. Adaptive and intelligent control of a dual-arm space robot for target manipulation during the post-capture phase. Aerospace Science and Technology, v. 142, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Viviane Tabchoury de Barros Vieira, Luis Fernando de Almeida, Francisco Jose Grandinetti, Alvaro Manoel Sousa Soares
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
A submissão de originais para este periódico implica na transferência, pelos autores, dos direitos de publicação impressa e digital. Os direitos autorais para os artigos publicados são do autor, com direitos do periódico sobre a primeira publicação. Os autores somente poderão utilizar os mesmos resultados em outras publicações indicando claramente este periódico como o meio da publicação original.