AI RESEARCH
HEP Statistical Inference for UAV Fault Detection: CLs, LRT, and SBI Applied to Blade Damage
arXiv CS.LG
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ArXi:2603.18546v1 Announce Type: new 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.