燃油流量信号是反映发动机状态和计算飞机排放物排放量的重要信号，但飞机飞行过程中传感器采集信号时不可避免地会受到外界环境以及内部因素干扰。提出一种结合样本熵（sample entropy, SE）的完全自适应噪声集合经验模态分解（complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN）与小波变换（wavelet transform, WT）的联合降噪方法。首先使用CEEMDAN对燃油流量信号进行分解得到本征模态分量，利用样本熵筛选含噪分量，并用相关系数与方差贡献率进行复核。对于含噪分量使用小波阈值降噪进行处理。最后将未处理的模态分量和完成降噪的模态分量重构得到最终燃油流量信号。通过与其他方法比较，CEEMDAN-SE-WT方法拥有最高信噪比为85.287，降噪后燃油消耗总量与飞机总重变化最为接近，可以认为该方法较大程度保留了燃油流量信号中的有效特征，为后续计算民机排放物排放总量提供了良好的数据支持。

The fuel flow signal is an important signal which reflect the status of the engine and calculate the emissions of aircraft emissions. However, it is inevitable that the sensor will be disturbed by external environment and internal factors during the acquisition of signals in flight. A joint denoising method combining sample entropy (SE) with adaptive noise (CEEMDAN) and wavelet transform (WT) was proposed. Firstly, CEEMDAN was used to decompose the fuel flow signal to obtain the intrinsic mode components, and the noise components were screened by sample entropy, checked by the correlation coefficient and variance contribution rate. Wavelet threshold denoising was used to process the model components containing noise. Finally, the unprocessed modal component and the denoising modal component were reconstructed to obtain the final fuel flow signal. Compared with other methods, the CEEMDAN-SE-WT has the highest signal-to-noise ratio of 85.287, and the total fuel consumption after noise reduction is closest to the change in the gross weight of the aircraft. It can be considered that this method retains the effective features in the fuel flow signal to a large extent, providing pure data support for calculating the total emissions of civil aircraft in the future.