Propeller Damage Detection, Classification, and Estimation in Multirotor Vehicles

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. Real flight data was collected by substituting one propeller with a damaged counterpart, representing three distinct damage types of varying severity. This data was then used to train a composite model, which included 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 were exclusively sourced from inertial measurements and control command inputs. This strategic choice ensures the adaptability of the proposed methodology across diverse multirotor vehicle platforms.