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딥러닝 기반 드론 블레이드의 고장예지에 관한 연구Prognostics of Drone Blade Based on Deep Learning
- Other Titles
- Prognostics of Drone Blade Based on Deep Learning
- Issue Date
- Sep-2023
- Publisher
- 한국기계가공학회
- Keywords
- Deep Learning(딥러닝); Drone(드론); Prognosti(고장예지); Prognostics and Health Management(건전성 예측 및 관리); Remaining Useful Life(잔여유효수명)
- Citation
- 한국기계가공학회지, v.22, no.9, pp.57 - 64
- Journal Title
- 한국기계가공학회지
- Volume
- 22
- Number
- 9
- Start Page
- 57
- End Page
- 64
- ISSN
- 1598-6721
- Abstract
- Confidential information, such as the reconnaissance system and mission information in a military drone, mayleak if a crack occurs in the blade during operation and the drone crashes. Major accidents, such as thoseinvolving humans, can happen when a commercial drone crashes. Therefore, minimizing the damage by earlyidentification of blade cracks before a crash is necessary. In this study, the failure mode was analyzed usingthe PHM(Prognostics and Health Management) method, RPN(Risk Priority Number) was identified through theFMECA(Failure Mode Effect and Critical Analysis) of the drone, and differences between the conditions weredetermined after obtaining vibration data under the normal and abnormal conditions identified. In addition,data features were extracted using a statistical method. A method to minimize drone loss was also proposedby predicting the vibration state of the drone according to the length of the blade crack using deep learning
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Related Researcher
- Hur, Jang Wook
- College of Engineering (School of Mechanical System Engineering)