关键词: 随机森林, 飞行状态识别, 预处理, 特征参数, 小样本

Abstract: In view of the problem of low recognition rate due to insufficient training samples, a flight condition recognition method based on random forest is proposed. Firstly, the flight data undergo preprocessing by outlier removal, clipping, and smoothing and the flight condition is classified into eight categories according to the characteristic parameters. Secondly, taking advantage of high recognition rate characteristics of random forest, a random forest classifier is designed for each category. Finally, every random forest classifier is trained with training samples, and all flight conditions of the helicopter are identified by the trained random forest classifier. Taking a helicopter real flight data as the experimental data, actual flight experiments show that, compared with the RBF neural network method and SVM method, the proposed method can significantly improve recognition rate and recognition speed is also improved under a small sample condition. It provides a reference for helicopter life prediction.

Key words: random forest, flight condition recognition, preprocessing, characteristic parameters, small sample