Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis
This work addresses UAV reliability for smart city infrastructure, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of detecting rotor blade defects in UAVs using vibration analysis and machine learning, achieving perfect detection of defective rotor blades with the Random Forest algorithm.
Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. To this end, the work presented in this paper leverages signal processing and Machine Learning (ML) methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects during pre and post-flight operation. With the help of dimensionality reduction techniques, the Random Forest algorithm exhibited the best performance and detected defective rotor blades perfectly. Additionally, a comprehensive analysis of the impact of various feature subsets is presented to gain insight into the factors affecting the model's classification decision process.