将粒子物理统计方法引入无人机螺旋桨故障检测，实现精准定位与不确定性量化。
HEP Statistical Inference for UAV Fault Detection: CLs, LRT, and SBI Applied to Blade Damage
- 融合LRT、CLs与SNPE三类方法，基于谐波特征进行故障判断
- 在18次飞行数据上达AUC 0.862，5%误报率下检出93%严重损伤
- 输出含置信区间的概率分布，适合高可靠性场景使用
本文将粒子物理中的三种统计方法应用于多旋翼螺旋桨故障检测：用于二元检测的似然比检验（LRT）、用于控制误报率的修正频数派方法（CLs），以及用于定量故障表征的序列神经后验估计（SNPE）。系统基于与旋翼谐波物理相关的频谱特征，输出三类结果：二元检测结果、受控的误报率、以及故障严重度和电机位置的校准后验分布。在包含18次真实飞行的六旋翼故障数据集UAV-FD上，采用留一飞行交叉验证，AUC为0.862±0.007（95%置信区间：0.849–0.876），优于CUSUM（0.708±0.010）、自编码器（0.753±0.009）和LSTM自编码器（0.551）。在5%误报率下，系统可检测到93%的显著损伤和81%的轻微损伤。在四旋翼平台PADRE上，仅重拟合生成模型后，AUC提升至0.986。SNPE对故障严重度的后验估计具有90%可信区间覆盖率（92–100%），平均绝对误差仅为0.012，输出包含不确定性而非单一点估计或故障标志。逐飞行序列检测实现100%故障检出，整体准确率达94%。
原文摘要 · Abstract (English)
This paper transfers three statistical methods from particle physics to multirotor propeller fault detection: the likelihood ratio test (LRT) for binary detection, the CLs modified frequentist method for false alarm rate control, and sequential neural posterior estimation (SNPE) for quantitative fault characterization. Operating on spectral features tied to rotor harmonic physics, the system returns three outputs: binary detection, controlled false alarm rates, and calibrated posteriors over fault severity and motor location. On UAV-FD, a hexarotor dataset of 18 real flights with 5% and 10% blade damage, leave-one-flight-out cross-validation gives AUC 0.862 +/- 0.007 (95% CI: 0.849--0.876), outperforming CUSUM (0.708 +/- 0.010), autoencoder (0.753 +/- 0.009), and LSTM autoencoder (0.551). At 5% false alarm rate the system detects 93% of significant and 81% of subtle blade damage. On PADRE, a quadrotor platform, AUC reaches 0.986 after refitting only the generative models. SNPE gives a full posterior over fault severity (90% credible interval coverage 92--100%, MAE 0.012), so the output includes uncertainty rather than just a point estimate or fault flag. Per-flight sequential detection achieves 100% fault detection with 94% overall accuracy.
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