民用发动机状态混沌预测算法

宋云雪; 彭朋; 史永胜

民用发动机状态混沌预测算法

中国民航大学航空工程学院,天津 300300

基金项目: 国家自然科学基金委员会与中国民用航空总局联合资助(60879017); 天津市自然科学基金(08JCYBJC11600)

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出版历程
- 收稿日期: 2010-01-30
- 修回日期: 2010-06-13
- 刊出日期: 2011-03-28

Chaos arithmetic for civil aero-engine forecasting

College of Aeronautical Engineering, Civil Aviation University of China,Tianjin 300300,China

摘要

摘要: 首先应用Haar小波和DB16小波对航空发动机排气温度的原始数据序列进行去噪处理,并且证明了处理后的数据序列具有混沌特征.其次应用混沌理论建立发动机状态预测算法,实现对排气温度的预测.通过检验排气温度预测值是否超过所规定的红线,以及该曲线是否平稳,从而进行发动机的健康状态排查.作为验证实例,使用一组某机型发动机实际飞行数据对预测算法进行了验证,并与加权一阶局域算法和自回归滑动平均模型算法进行了对比.结果表明,该组合模型算法精度优于加权一阶局域算法和自回归滑动平均模型算法,该方法可以为这种机型发动机故障预测提供决策依据.
- 航空发动机 /
- 小波变换 /
- 排气温度 /
- 预测算法 /
- 加权一阶局域算法
Abstract: Firstly,using Haar wavelet and DB16 wavelet,the original exhaust gas temperature data series were denoised.Further analysis on the denoised data series indicates the existence of a chaos feature.Then,by using chaos theory,chaotic forecasting arithmetic was established to forecast the exhaust gas temperature data series.Finally,by testing the stable level of data series and comparing the series with the red line,the condition of aero-engine was defined.The proposed arithmetic was verified through some types of aircraft aero-engine exhaust gas temperature data series obtained from actual flight,and then compared with adding-weight one-rank local-region arithmetic and auto-regressive and moving average (ARMA) arithmetic.The result shows that the proposed arithmetic has a better forecasting accuracy.It can be used as a supporting model in the decision-making of aero-engine fault forecasting.
- aero-engine /
- wavelet transformation /
- exhaust gas temperature /

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参考文献(12)

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