Earticle

초록

영어

This research is to study the solution to the defects in maintenance and inspection that can be predicted/prevented in advance among human factors that account for more than 70% of the causes of aviation accidents. Traditionally, mechanics have performed visual inspections of aircraft exteriors. Due to this, there were factors that affect the human ability of mechanics in aircraft maintenance and inspection, safety problems when performing the upper part of the aircraft inspection, and the difficulty of precise inspection. To improve these problems, we conduct a study on an AI drone inspection system that has deep-learned samples on aircraft damage/defects. In this paper, we describe the aircraft maintenance inspection checklist, non-destructive inspection types, types of aircraft damage and defects, deep-learning highly reliable AI drone inspection systems, and the expected effects of this technology and future applications. Through this system research, it is expected that mechanics will efficiently inspect the aircraft through the optimization of aircraft maintenance system technology to prevent aviation accidents in advance and reduce time and economic costs.

목차

ABSTRACT
1. 서론
2. 항공기 정비 검사 및 결함
2.1 항공기 정비검사의 방법 및 범위
2.2 항공기 정비검사 종류
2.3 결함의 종류
3. 드론을 활용한 항공기 정비검사
3.1 드론을 활용한 딥러닝 과정
3.2 항공기 정비검사 시스템 구성 및 운영
3.3 제안한 시스템의 성능 비교
4. 결론
References

키워드

항공기 드론 검사) 감항성 인공지능 손상 결함 인적요인) 예방정비 Aircraft Drone Inspection Airworthiness AI Damage Fault Human factors Preventive maintenance

저자

• 이윤서 [ Yun-Seo Lee | Member, Researcher, TES Co., Jinju, Korea ]
• 이정훈 [ Jung-Hoon Lee | Member, professor, Dept. of Control & Instrument Engineering, Gyeongsang National University ] Corresponding Author

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