Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter

Wei-Hsiang Lai,Sung-Ting Tsai,De-Li Cheng,Yih-Rong Liang

doi:10.3390/app112110297

Abstract

The aim of this study was to examine the health diagnosis classification method of quadcopter structures with different mixed faults. The loosening of the motor mount, damage to the propeller, and the loosening of the arm mount were the main fault conditions investigated. Data were first acquired under non-fault conditions and the conditions of the three types of fault. Then, the features of the vibration and pulse width modulation signals were extracted by root mean square, standard deviation, and sample entropy. Moreover, the features of the audio signal were extracted by wavelet scattering, which contains time-frequency domain information that provides significant power for classification. In this paper, we propose a simple machine learning method, based on the k-Nearest Neighbor (kNN), not only for classification but also demonstrating the efficacy of the features. To test the limits of accuracy, different configurations of kNN parameters are deployed, in addition to the features. In summary, as a result of the highly efficacious features, despite mixed fault conditions, the accuracy reached 90.73%. This method improves the accuracy of mixed faults’ classification and maintains a certain level of classification effectiveness.

Highlights

The development of unmanned aircraft systems is rapidly expanding
Two data sources were used to build the two ML models: (1) vibration and Remote control output (RCOU) signals obtained from the flight data files, as shown in Figure 5d–f; and (2) data based on the audio sensor signal, as shown in Figures 6 and 7
The model parameters that are most suitable for the flight data parameters of the quadcopter were found

Summary

Introduction

The development of unmanned aircraft systems is rapidly expanding. Using these systems, operators can perform and complete tasks, enhancing people’s lives. Drones provide convenience and benefits, and increase the risks associated with flight safety. Ensuring flight safety is a topic that is crucial to the use of drones. This study was dedicated to a health diagnosis classification system of quadcopter structures. Quadcopters are composed of motors, propellers, arms, landing gear, a fuselage, flight control computers, electronic transmissions, batteries, and signal receivers

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Methods

Results

Conclusion