基于EWT-熵值方法的发动机风扇叶片损伤监控

Damage monitoring of engine fan blades based on EWT-entropy method

为了从发动机性能数据中寻找风扇叶片外来物损伤航班特征,从而将风扇叶片受外来物损伤的航班区分出来,在机载快速存储记录器(quick access recorder, QAR)数据检测中提出将经验小波分解和信息熵结合的方法。通过对各航班原始振动数据的拟合平滑处理和经验小波分解,提取了分解后各模态的能量熵和,分析了添加汉明(Hanmming)窗函数的多尺度熵,结果表明:拟合后数据的熵值变化更明显,且风扇叶片受外来物损伤航班的能量熵和有10%以上的降低趋势,改进后的多尺度熵有40%以上的增长趋势,均明显异于其他正常航班。证明采用EWT-熵值方法可以较好地对发动机风扇叶片外来物损伤情况进行监控。

In order to find the characteristics of the flight damaged by foreign objects on the fan blades from the engine performance data,so as to distinguish the flights damaged by the foreign objects on the fan blades,a method combining empirical wavelet transform and information entropy was proposed in the airborne quick access recorder(QAR)data detection. Through the fitted and smoothed process and empirical wavelet transform of the original vibration data of each flight,the sum of the energy entropy of each intrinsic mode function after decomposition was extracted,and the multi-scale entropy of the added window function was analyzed. The result showed the entropy value of the fitted data changed more obviously,and the sum of the energy entropy of the flight with fan blades damaged by foreign objects had a decreasing trend of more than 10%,and the improved multi-scale entropy had an increasing trend of more than 40%, which was obviously different from other normal flights. It is proved that the EWTentropy method can better monitor the damage by foreign objects of the engine fan blades.