自主垂直起降飞行器故障检测、隔离与严重程度预测的机器学习框架

中文摘要 AI 辅助

自主垂直起降（VTOL）飞行器的故障检测至关重要，因为即使是微小的部件退化也可能迅速使在复杂、安全关键环境中运行的多旋翼飞行器失稳，这促使需要能够识别旋翼损伤早期迹象的鲁棒故障检测与估计策略；然而，由于传感器噪声、环境扰动以及多旋翼平台的非线性空气动力学特性，真实飞行中的故障检测仍然具有挑战性。本研究提出了一种利用真实飞行数据进行旋翼故障检测、隔离和严重程度预测的综合机器学习框架。开发了一种卷积神经网络（CNN）架构，用于从多变量飞行动力学中学习时空模式，从而能够直接推断故障旋翼及其损伤程度。该框架首先使用由数据生成模型生成的模拟数据进行验证，然后通过在六旋翼飞行器上引入受控的桨尖断裂进行实验验证。训练后的模型在实验数据中实现了旋翼级故障分类准确率超过99%，以及在1%容差内的严重程度估计准确率达到96%，展示了强大的泛化能力，并支持自主VTOL系统的实时健康监测。

英文摘要

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.

发表机构

• Oklahoma State University（俄克拉荷马州立大学）
• SRI International（SRI国际）

机构由 AI 辅助整理，请以论文原文为准。