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

2026-09-12

AI Summary

研究提出了一种基于卷积神经网络的机器学习框架，用于自主VTOL飞机的故障检测、隔离及严重程度预测，解决了复杂环境下的早期故障识别问题。

Abstract

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.

Problem

• Fault detection
• Autonomous VTOL aircraft
• Sensor noise
• Environmental disturbances
• Nonlinear aerodynamics

Innovation

• Machine Learning Framework
• Fault Detection and Isolation
• Severity Prediction
• Convolutional Neural Network (CNN)
• Real-time Health Monitoring