Application of self-organizing map on flight data analysis for quadcopter health diagnosis system

De Li Cheng, Wei Hsiang Lai

研究成果: Conference article同行評審

1 引文 斯高帕斯(Scopus)

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.

原文English
頁(從 - 到)241-246
頁數6
期刊International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
42
發行號2/W13
DOIs
出版狀態Published - 2019 六月 4
事件4th ISPRS Geospatial Week 2019 - Enschede, Netherlands
持續時間: 2019 六月 102019 六月 14

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

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