UAV故障検出のためのHEP統計的推論：CLs、LRT、SBIをブレード損傷に適用
HEP Statistical Inference for UAV Fault Detection: CLs, LRT, and SBI Applied to Blade Damage
粒子物理学の統計手法（LRT、CLs、SNPE）をマルチローターのプロペラ故障検出に転用し、ブレード損傷の検出・誤警報率制御・故障度合いの不確実性付き推定を実現した。
著者: Khushiyant
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.