Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Fault Diagnosis of Drone Using Machine Learning

머신러닝을 이용한 드론의 고장진단에 관한 연구

  • Park, Soo-Hyun (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Do, Jae-Seok (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Choi, Seong-Dae (Department of Mechanical System Engineering, Kumoh National institute of Technology) ;
  • Hur, Jang-Wook (Department of Mechanical System Engineering, Kumoh National institute of Technology)
  • 박수현 (금오공과대학교 기계시스템공학과) ;
  • 도재석 (금오공과대학교 기계시스템공학과) ;
  • 최성대 (금오공과대학교 기계시스템공학과) ;
  • 허장욱 (금오공과대학교 기계시스템공학과)
  • Received : 2021.05.27
  • Accepted : 2021.07.19
  • Published : 2021.09.30
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Abstract

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

Keywords

Acknowledgement

이 논문은 2019년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. 2019R1I1A3A01063935).

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