APPLICATION OF SELF-ORGANIZING MAP ON FLIGHT DATA ANALYSIS FOR QUADCOPTER HEALTH DIAGNOSIS SYSTEM
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/241/2019/isprs-archives-XLII-2-W13-241-2019.pdf |
| Summary: | 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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| ISSN: | 1682-1750 2194-9034 |