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
N1 - Funding Information:
The authors would like to pay appreciation to Ministry of Science and Technology under MOST106-2622-E-006-033-CC2 to provide the funding for this project.
Publisher Copyright:
© Authors 2019.
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.
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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
T2 - 4th ISPRS Geospatial Week 2019
Y2 - 10 June 2019 through 14 June 2019
ER -