基于功能合并模型和贝叶斯量化的多旋翼螺旋桨损伤诊断统一统计框架：实验飞行测试评估
A Unified Statistical Framework for Multicopter Propeller Damage Diagnosis Based on Functionally Pooled Models and Bayesian Quantification: Experimental Flight Test Assessment
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中文总结 AI 辅助
研究多旋翼螺旋桨损伤诊断，基于功能合并自回归模型的框架，仅用惯性测量单元数据，经实验飞行测试评估，能进行损伤检测、识别与量化，相比传统方法更稳定，为结构健康监测提供高效严谨方案。
中文摘要 AI 辅助
本文介绍了一种基于随机时间序列的框架，用于使用功能合并自回归（FP-AR）模型进行多旋翼螺旋桨损伤诊断。该框架仅使用标准惯性测量单元数据就能解决损伤检测、电机级识别和损伤幅度估计问题，无需额外传感器。功能合并提供了跨不同运行条件的系统动力学紧凑表示，并支持从短数据记录中进行可靠的模型估计。损伤检测通过预测残差的统计测试进行，损伤识别通过模型选择进行，损伤量化通过贝叶斯推理方案提供后验估计和不确定性界限。通过在环境风干扰下遵循八字形轨迹的定制六旋翼无人机的户外飞行测试对该框架进行了实验评估。分析了多个电机和螺旋桨损伤水平下的六个IMU通道，包括三轴加速度和角速度。结果表明，该框架具有一致的跨飞行性能，无需针对特定情况进行重新调整。与传统的基于批次的量化相比，贝叶斯方法提供了更稳定的估计和明确的不确定性表征。总体而言，所提出的框架为多旋翼结构健康监测提供了一种数据高效、可解释且统计严格的解决方案。
英文摘要
In this work, a stochastic time series-based framework is introduced for multicopter propeller damage diagnosis using functionally pooled autoregressive (FP-AR) models. The framework addresses damage detection, motor-level identification, and damage magnitude estimation using only standard inertial measurement unit data, without requiring additional sensors. Functional pooling provides a compact representation of system dynamics across varying operating conditions and supports reliable model estimation from short data records. Damage detection is performed through statistical testing of prediction residuals, damage identification through model selection, and damage quantification through a Bayesian inference scheme that provides posterior estimates and uncertainty bounds. The framework is experimentally evaluated through outdoor flight tests of a custom-built hexacopter following figure-eight trajectories under ambient wind disturbances. Six IMU channels, including three-axis acceleration and angular velocity, are analyzed across multiple motors and propeller damage levels. The results demonstrate consistent cross-flight performance without case-specific retuning. Compared with conventional batch-based quantification, the Bayesian approach provides more stable estimates and explicit uncertainty characterization. Overall, the proposed framework offers a data-efficient, interpretable, and statistically rigorous solution for multicopter structural health monitoring.
