Although ensemble empirical mode decomposition (EEMD) can suppress the modal confusion phenomenon in the EMD method to a certain extent, the added white noise cannot be completely neutralized. The complementary EEMD (CEEMD) adds white noise with opposite signs to the analysis signal in pairs, which greatly reduces the reconstruction error.

Aiming at the problems of modal confusion and the difficulty in accurately extracting fault features of rolling bearings, a CPCEEMD (CEEMD-PE-CEEMD) bearing fault diagnosis method is proposed, which fully combines the CEEMD algorithm and the advantages of signal randomness detection based on permutation entropy (PE). After the abnormal components of the CEEMD are detected by permutation entropy, CEEMD of the remaining signals is conducted directly. Intrinsic mode function (IMF) components with large correlation coefficients are selected for Hilbert envelope spectrum analysis, and fault features are extracted from the envelope diagram. By analyzing the simulation signal and the measured bearing signal, the results show that the proposed method has a good decomposition effect, results in a certain inhibition effect on the modal confusion in the EMD process, and can effectively extract the characteristic information of the rolling bearing fault signal, which is feasible.

尽管集成的经验模态分解（EEMD）可以在一定程度上抑制EMD方法中的模态混淆现象，但是添加的白噪声无法完全消除。互补EEMD（CEEMD）成对地将具有相反符号的白噪声添加到分析信号中，从而大大降低了重建误差。

针对模态混淆问题和滚动轴承故障特征难以准确提取的问题，提出了一种将CEEMD算法与基于CEEMD算法的信号随机性检测的优点相结合的CPCEEMD（CEEMD-PE-CEEMD）轴承故障诊断方法。排列熵（PE）。在通过置换熵检测到CEEMD的异常分量之后，直接对其余信号进行CEEMD。选择具有大相关系数的本征模函数（IMF）分量进行希尔伯特包络频谱分析，并从包络图中提取故障特征。通过对仿真信号和测得的方位信号的分析，结果表明该方法具有良好的分解效果，