Transactions of Nanjing University of Aeronautics & Astronautics
An Aircraft Icing Detection Method Based on Performance Data of Rotor
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Abstract:
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.
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Project Supported:
This work was supported in part by the National Key R&D Program of China （No.2022YFE02-03700）， and the Aeronautical Science Foundation of China （No. 2023Z010027001）. The authors would like to acknowledge the following people for their assistance： GUO Jin， LIU Nan， JIANG Xinkai， ZHANG Jinlong， and NIE Xin.
Clc Number:
V244.15
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