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 |
| 卷 | 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 10 → 2019 六月 14 |
指紋
All Science Journal Classification (ASJC) codes
- Information Systems
- Geography, Planning and Development
引用此文
}
Application of self-organizing map on flight data analysis for quadcopter health diagnosis system. / Cheng, De Li; Lai, Wei Hsiang.
於: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 卷 42, 編號 2/W13, 04.06.2019, p. 241-246.研究成果: Conference article
TY - JOUR
T1 - Application of self-organizing map on flight data analysis for quadcopter health diagnosis system
AU - Cheng, De Li
AU - Lai, Wei Hsiang
PY - 2019/6/4
Y1 - 2019/6/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067474892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067474892&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLII-2-W13-241-2019
DO - 10.5194/isprs-archives-XLII-2-W13-241-2019
M3 - Conference article
AN - SCOPUS:85067474892
VL - 42
SP - 241
EP - 246
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 2/W13
ER -