Propeller damage detection, classification and estimation in multirotor vehicles

TL;DR

This paper presents a data-driven neural network framework for detecting, classifying, and estimating damage in multirotor UAV propellers using flight data, enhancing maintenance and safety protocols.

Contribution

It introduces a novel architecture that combines classifiers and neural networks trained on inertial and control data for damage assessment in UAV propellers.

Findings

Accurately identifies damage types and severity levels.

Pinpoints specific damaged rotors.

Uses only inertial and control data for broad applicability.

Abstract

This manuscript details an architecture and training methodology for a data-driven framework aimed at detecting, identifying, and quantifying damage in the propeller blades of multirotor Unmanned Aerial Vehicles. By substituting one propeller with a damaged counterpart-encompassing three distinct damage types of varying severity-real flight data was collected. This data was then used to train a composite model, comprising both classifiers and neural networks, capable of accurately identifying the type of failure, estimating damage severity, and pinpointing the affected rotor. The data employed for this analysis was exclusively sourced from inertial measurements and control command inputs, ensuring adaptability across diverse multirotor vehicle platforms.