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.

翻译：本文将粒子物理中的三种统计方法迁移至多旋翼螺旋桨故障检测：用于二值检测的似然比检验（LRT）、用于虚警率控制的CLs修正频率学派方法，以及用于故障定量表征的序贯神经后验估计（SNPE）。该系统基于与转子谐波物理特性相关的频谱特征运行，输出三类结果：二值检测结果、受控虚警率，以及关于故障严重程度与电机位置的标定后验分布。在包含18次真实飞行数据（含5%和10%叶片损伤）的六旋翼数据集UAV-FD上，留一飞行交叉验证得到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%虚警率条件下，系统对显著叶片损伤和轻微叶片损伤的检出率分别达到93%和81%。在四旋翼平台PADRE上，仅对生成模型进行重拟合后AUC达到0.986。SNPE可提供故障严重程度的完整后验分布（90%置信区间覆盖率为92–100%，MAE为0.012），其输出结果包含不确定性信息，而非仅为点估计或故障标志。逐次飞行的序贯检测实现了100%故障检出率与94%总体准确率。