The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 241–246, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-241-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 241–246, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-241-2019

  04 Jun 2019

04 Jun 2019

APPLICATION OF SELF-ORGANIZING MAP ON FLIGHT DATA ANALYSIS FOR QUADCOPTER HEALTH DIAGNOSIS SYSTEM

D.-L. Cheng and W.-H. Lai D.-L. Cheng and W.-H. Lai
  • Dept. of Aeronautics & Astronautics, National Cheng Kung University, No.1 Univ. Rd., Tainan, Taiwan

Keywords: Fault Detection, Health Monitoring, UAS, Machine Learning, Self-Organizing Map, Vibration Analysis

Abstract. The UAS fault problem has led to many potential risk factors behind its rapid development in recent years. Therefore, the diagnosis of UAS health status is still an important issue. This study adopted the SOM machine learning method which is an unsupervised clustering method to establish a model for diagnosing the health status of quadcopter. Take the vibration features of three flight states (undamaged, motor mount loose, unbalanced broken propeller). Through those training data the model can cluster different vibration pattern of fault situation. It not only can classify the failure status with 99% accuracy but also can provide pre-failure indicators.