Application of Self-Organizing Map on Flight Data Analysis for Quadcopter Health Diagnosis System

Abstract

The development of drones is booming day by day it brings convenience and benefit but it also makes a lot of risk of safety Therefore health diagnosis is a crucial issue about drones This study dedicates to health diagnosis system of structure of quadcopter Loosening of motor mount and propeller broken are the mainly discussed fault conditions used in this study In the beginning of the research the data of the undamaged loosening of motor mount and propeller broken are acquired Then the features of vibration signal are extracted by three methods root mean square standard deviation and sample entropy respectively Next Self-Organizing Map (SOM) model can be trained by using features which are extracted by the vibration signal SOM is an unsupervised machine learning method and it’s a type of neuron network After training by SOM model the regulation of high dimensionality data can be found and neurons of SOM can also preserve the topological property of data Then KNN (K-Nearest Neighbor) is used to apply SOM model and do fault classification and fault level recognition The first result shows the good performance of model which can exceed 96% of precision if the test data is similar with train data However the model can’t classify slight fault condition truly Because of that this study proposed hierarchical diagnosis model In the first layer distance comparison is used to find low gap train data and SOM model is trained by selected data First layer model only does fault classification In the second layer SOM model is trained by only two conditions undamaged and one fault condition Without the interaction of other fault condition this model can recognize the fault level by the fault recall rate By hierarchical diagnosis model the new classification model has the bigger classification range and the fault recall rate of second layer can be an indicator as the fault level

Date of Award	2019
Original language	English
Supervisor	Wei-Hsiang Lai (Supervisor)