针对旋转机械故障数据的非平稳性及总体平均经验模态分解方法（CEEMD）舍弃高频分量降噪方法和小波阈值降噪方法存在的不足，提出了基于CEEMD和排列熵的小波阈值降噪方法。运用CEEMD将信号分解为一系列的固有模态函数（IMF）分量，利用排列熵来确定含有噪声成分较多的IMF分量，采用小波阈值降噪方法对含有较多噪声成分的IMF分量进行降噪处理，保留这些分量中的有效信息。仿真分析和实例分析表明，基于CEEMD和排列熵的小波阈值降噪方法效果优于单纯的CEEMD降噪方法和小波阈值降噪方法。

Considering the non-stationary of rolling machinery fault data, the wavelet threshold denoising method based on CEEMD and Permutation Entropy (PE)is proposed to cover the shortage of Complementary Ensemble Empirical Mode Decomposition (CEEMD) denoising method and wavelet threshold denoising method. CEEMD is used to decompose signals into a series of IMF components, the Permutation Entropy is presentedto evaluate noise contained in each IMF component, and wavelet threshold method is adopted for IMF components containingmuch noise for denoising, in order to retain the useful information of these components. The test results ofsimulation data and experimental data show that, the wavelet threshold denoising method based on CEEMD and PE is better than pure CEEMD denoising method and the wavelet threshold denoising method.