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

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Cheng, D.-L.^1¹ 

[1] Dept. of Aeronautics & Astronautics, National Cheng Kung University, No.1 Univ. Rd., Tainan, Taiwan^1

关键词: Fault Detection;    Health Monitoring;    UAS;    Machine Learning;    Self-Organizing Map;    Vibration Analysis;   

DOI  :  10.5194/isprs-archives-XLII-2-W13-241-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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.

【 授权许可】

CC BY