# A Machine Learning Framework for Fault Detection, Isolation, and Severity Prediction of Autonomous VTOL Aircraft

arXiv: https://arxiv.org/abs/2609.14180 · HTML: https://mlsanity.com/paper/2609.14180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture](https://mlsanity.com/paper/2004.14421/) (2020)
- [On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification](https://mlsanity.com/paper/2005.00336/) (2020)
- [Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model](https://mlsanity.com/paper/2302.01556/) (2023)
- [Propeller damage detection, classification and estimation in multirotor vehicles](https://mlsanity.com/paper/2410.05447/) (2024)

## Abstract (arXiv)

Fault detection in autonomous VTOL aircraft is critical because even minor component degradations can rapidly destabilize multirotor vehicles operating in complex, safety-critical environments, motivating robust fault detection and estimation strategies capable of identifying early signs of rotor damage; however, real-flight fault detection remains challenging due to sensor noise, environmental disturbances, and the nonlinear aerodynamics 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 faulted rotor and its damage level. The framework is first validated using simulated data generated by a data-generative model, and experimental validation is then performed on a hexacopter by introducing controlled blade-tip breakage. The trained model achieves rotor-wise fault classification accuracies above 99% and 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.