Application of Wavelet Scattering and Machine Learning on Flight Data Analysis for Quadcopter Diagnosis Classification System

Abstract

The development of unmanned aircraft systems is booming vigorously Drone brings convenience and benefit but it also makes risk of flight safety Therefore how to control the risk of flight safety is a crucial study about drones This study dedicates to health diagnosis classification system of structure of quadcopter Loosening of motor mounts propeller broken and loosening of arm mounts 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 propeller broken and loosening of arm mount are acquired Then the features of vibration signal and pulse width modulation signal are extracted by three methods root mean square standard deviation and sample entropy respectively Moreover the features of audio signal are extracted by wavelet scattering Next kNN (k-Nearest Neighbor) model can be trained by using features which are extracted by the vibration signal and it is a supervised machine learning method After training by kNN model kNN model can do fault classification and regression analysis The study proposes the method of time and frequency domain analysis on flight data analysis and establishment of quadcopter diagnosis classification system It can classify multiple failure types in quadcopter which include no failure single fault double faults and triple faults In the case of classifying triple faults the classification system has an accuracy of 90 73% This method improves the accuracy of mixed faults classification and maintains a certain level of classification effectiveness

Date of Award	2020
Original language	English
Supervisor	Wei-Hsiang Lai (Supervisor)