A Machine Learning Framework for Fault Detection, Isolation, and Severity Prediction of Autonomous VTOL Aircraft
TL;DR
A convolutional neural network architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level and supporting real-time health monitoring for autonomous VTOL systems.
Abstract
Fault detection in autonomous VTOL aircraft is critical because even minor degradations can quickly destabilize multirotor vehicles in safety-critical environments. However, real-flight fault detection remains challenging due to sensor noise, environmental disturbances, and the nonlinear aeromechanics of multirotor platforms. This study proposes a comprehensive machine-learning framework for rotor fault detection, isolation, and severity prediction using real flight data. A convolutional neural network (CNN) architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level. The framework is first validated using simulated data generated by our in-house flight dynamic model. Next, to verify the framework using real flight data, a hexcopter was designed, fabricated and flight tested for both nominal and faulty cases by introducing controlled blade-tip breakage. The trained model achieves rotor-wise fault classification accuracies above 99% and sample-wise severity estimation accuracy of 96% within a ±1% tolerance in experimental data, demonstrating strong generalization and supporting real-time health monitoring for autonomous VTOL systems.
