APPLICATION OF SELF-ORGANIZING MAP ON FLIGHT DATA ANALYSIS FOR QUADCOPTER HEALTH DIAGNOSIS SYSTEM
Autor: | De Li Cheng, Wei Hsiang Lai |
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Rok vydání: | 2019 |
Předmět: |
lcsh:Applied optics. Photonics
Self-organizing map Quadcopter Training set lcsh:T business.industry Computer science Propeller lcsh:TA1501-1820 Machine learning computer.software_genre Fault (power engineering) Cluster (spacecraft) lcsh:Technology Fault detection and isolation lcsh:TA1-2040 Artificial intelligence lcsh:Engineering (General). Civil engineering (General) Health diagnosis business Flight data computer |
Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W13, Pp 241-246 (2019) |
ISSN: | 2194-9034 |
DOI: | 10.5194/isprs-archives-xlii-2-w13-241-2019 |
Popis: | 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. |
Databáze: | OpenAIRE |
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