Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis

TL;DR

This paper explores the use of machine learning and signal processing techniques to detect UAV rotor blade defects through vibrational data analysis, emphasizing the effectiveness of Random Forest classifiers and feature subset analysis.

Contribution

It introduces a novel approach combining vibrational analysis with machine learning for UAV rotor defect detection, highlighting the effectiveness of Random Forest and feature analysis.

Findings

Random Forest achieved perfect defect detection.

Dimensionality reduction improved model performance.

Feature subset analysis provided insights into classification factors.

Abstract

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.