Uniform phase empirical mode decomposition (UPEMD) is an effective signal separation method, which is proposed to solve the mode mixing phenomenon of empirical mode decomposition (EMD) by adding uniform phase sinusoidal signal as masking signal. However, the decomposition effect of UPEMD depends on the selection of amplitude and phase number of masking signal. Besides, there are also some issues such as noise residue and incomplete decomposition that need to be solved. In this paper, a novel permutation entropy-based improved uniform phase empirical mode decomposition (PEUPEMD) method is proposed to address these problems. In PEUPEMD method, first of all, the sinusoidal signals with uniform phases are added to the raw signal as masking signals. Second, the superimposed signal is decomposed using EMD and the final intrinsic mode functions are obtained via an ensemble way. Third, the obtained IMFs with high-frequency are detected by permutation entropy algorithm. Finally, the residual signal containing low-frequency components is decomposed through EMD completely. Last, the simulated signal and tested data are applied to verify the feasibility of PEUPEMD via comparing it with EMD, UPEMD, CEEMDAN and ICEEMDAN methods. The results analysis indicated that PEUPEMD was superior to the comparative methods in decomposing accuracy and mode mixing suppression.

均匀相位经验模态分解（UPEMD）是一种有效的信号分离方法，它是为解决经验模态分解（EMD）中的模式混合现象而提出的，通过加入均匀相位正弦信号作为掩蔽信号。但是，UPEMD 的分解效果取决于掩蔽信号幅度和相位数的选择。此外，还存在噪声残留、分解不完全等问题需要解决。在本文中，提出了一种新的基于置换熵的改进均匀相位经验模式分解（PEU​​PEMD）方法来解决这些问题。在PEUPEMD方法中，首先将具有均匀相位的正弦信号作为掩蔽信号添加到原始信号中。第二，使用 EMD 分解叠加信号，并通过集成方式获得最终的本征模式函数。第三，通过置换熵算法检测获得的高频IMF。最后，包含低频分量的残差信号通过 EMD 完全分解。最后，通过与EMD、UPEMD、CEEMDAN和ICEEMDAN方法的比较，应用仿真信号和测试数据验证PEUPEMD的可行性。结果分析表明，PEUPEMD在分解精度和模态混合抑制方面优于对比方法。通过与EMD、UPEMD、CEEMDAN和ICEEMDAN方法的比较，应用仿真信号和测试数据验证PEUPEMD的可行性。结果分析表明，PEUPEMD在分解精度和模态混合抑制方面优于对比方法。通过与EMD、UPEMD、CEEMDAN和ICEEMDAN方法的比较，应用仿真信号和测试数据验证PEUPEMD的可行性。结果分析表明，PEUPEMD在分解精度和模态混合抑制方面优于对比方法。