航空发动机性能参数预测方法

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.

Forecasting method for aeroengine performance parameters

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.