An Aircraft Icing Detection Method Based on Performance Data of Rotor

Existing icing detection technologies face challenges when applied to small and medium-sized aircraft， especially electric vertical take-off and landing （eVTOL） aircraft that meet the needs of low-altitude economic development. This study proposes a data-driven icing detection method based on rotor performance evolution. Through dry-air baseline tests and dynamic icing comparative experiments （wind speed 0—30 m/s， rotational speed 0—3 000 r/min， collective pitch 0°—8°） of a 0.6 m rotor in the FL-61 icing wind tunnel， a multi-source heterogeneous dataset containing motion parameters， aerodynamic parameters， and icing state identifiers is constructed. An innovative signal processing architecture combining adaptive Kalman filtering and moving average cascading is adopted. And a comparative study is conducted on the performance of support vector machine （SVM）， multilayer perceptron （MLP）， and random forest （RF） algorithms， achieving real-time identification of icing states in rotating components. Experimental results demonstrate that the method exhibits a minimum detection latency of 6.9 s and 96% overall accuracy in reserved test cases， featuring low-latency and low false-alarm， providing a sensor-free lightweight solution for light/vertical takeoff and landing aircraft.