中文

Journal of Applied Sciences ›› 2016, Vol. 34 ›› Issue (4): 469-474.doi: 10.3969/j.issn.0255-8297.2016.04.012

• Control and System • Previous Articles    

Recognition of Helicopter Flight Condition Based onSupport Vector Machine

XIONG Bang-shu1, LIU Yu1, MO Yan1, HUANG Jian-ping2, LI Xin-min2   

  1. 1. Key Laboratory of Image Processing and Pattern Recognition, of Jiangxi ProvinceNanchang Hangkong University, Nanchang 330063, China;
    2. National Key Laboratory of Rotorcraft Aerodynamics, China Helicopter Research andDevelopment Institute, Jingdezhen 333001, Jiangxi Province, China
  • Received:2015-11-10 Revised:2016-02-02 Online:2016-07-30 Published:2016-07-30

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Abstract:

To solve the problem of low recognition rate due to insufficient training samples, a flight condition recognition method based on SVM is proposed. The flight data first undergo denoising by clipping, outlier removal, and averaging. The changing rate of flight data is obtained with least square line fitting. Redundancy in the data is reduced based on the characteristic parameters extracted using linear correlation. The flight condition is classified into ten categories according to the characteristic parameters. An SVM classifier is designed for each category to improve identification efficiency. Finally, every SVM classifier is trained with training samples, and all flight conditions of the helicopter are identified by the trained SVM classifier. Actual flight experiments show that, compared with the RBF neural network method, the proposed method can improve performance under a small sample condition. It provides a reference for helicopter fault diagnoses and life prediction.

Key words: support vector machine (SVM), flight condition recognition, least squaremethod, linear dependence, small sample

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