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This paper adapts three statistical inference techniques from high-energy physics – likelihood ratio tests (LRT), CLs method, and sequential neural posterior estimation (SNPE) – to the problem of UAV propeller fault detection. The methods operate on spectral features derived from rotor harmonics to provide binary fault detection, controlled false alarm rates, and calibrated posterior distributions over fault severity and location. Experiments on hexarotor and quadrotor datasets demonstrate that the proposed approach outperforms baseline methods like CUSUM and autoencoders, achieving high AUC scores and accurate fault characterization with uncertainty quantification.
Particle physics techniques can give your drone superhuman senses: statistical methods from CERN enable UAVs to detect subtle blade damage with calibrated uncertainty, outperforming standard anomaly detection methods.
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.
