北京航空航天大学学报 ›› 2008, Vol. 34 ›› Issue (03): 253-256.

• 论文 •    下一篇

航空发动机性能参数预测方法

李晓白,崔秀伶,郎荣玲   

  1. 北京航空航天大学 电子信息工程学院, 北京 100083
  • 收稿日期:2007-06-29 出版日期:2008-03-31 发布日期:2010-09-17
  • 作者简介:李晓白(1953-),男,北京人,副教授,lxbbh3917@sina.com.
  • 基金资助:

    武器装备预研基金资助项目(51417010201HK0155)

Forecasting method for aeroengine performance parameters

Li Xiaobai, Cui Xiuling, Lang Rongling   

  1. School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2007-06-29 Online:2008-03-31 Published:2010-09-17

摘要: 航空发动机性能参数预测对于发动机的视情维修具有重要的意义.为了提高预测精度,在分析发动机性能参数数据特点的基础上,提出了一种新的应用于此领域的组合预测模型.首先利用小波变换将原始数据分解为不同尺度上的几组子序列,根据各子序列的特点分别选用自回归滑动平均(ARMA,Autoregressive Moving Average)模型或求和自回归滑动平均(ARIMA,Autoregressive Integrated Moving Average)模型进行预测,然后将所有预测结果合成,得到最终预测结果.通过仿真实验,验证了该组合模型提高短期和中长期预测精度的有效性,并分析了小波分解层数对于预测精度的影响.

Abstract: The forecasting of aeroengine performance parameters is very important for aeroengine maintenance based on condition. To improve the forecasting accuracy, a new combination method was proposed for forecasting parameters based on analyzing the data. Firstly, the original sequence was decomposed by wavelet transform and some sub-sequences in different frequency band were obtained. Then these sub-sequences were forecasted by ARMA/ARIMA respectively. Finally, the forecasting results of all sub-sequences were reconstructed and taken as the final forecasting result. Through the test, the proposed combination model was proved to be highly effective on improving the accuracy of the short-term and long-term forecasting, and the effect of wavelet decomposition levels on forecasting accuracy was analyzed.

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