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中文摘要：
针对经验模态分解（EMD）的模态混淆问题，提出了一种新的抑制模态混淆的方法---基于伪极值点假设的经验模态分解（PEEMD）。与总体平均经验模态分解（EEMD）通过添加白噪声再进行总体平均的方式不同，PEEMD通过定义最小极值尺度，并用其度量其他极值尺度，通过增加伪极值点的方式来均匀化尺度，有效地抑制了模态混淆的产生。详细介绍了PEEMD方法，并通过仿真信号将其与EMD和EEMD进行了对比，最后，将PEEM D应用于转子碰摩故障的诊断中。仿真和实测信号结果表明，PEEMD在分量的精确性和抑制模态混淆的产生等方面要优于EMD和EEMD ，是一种有效的信号分解方法。
英文摘要：
A novel method PEEMD was proposed for restraining the mode mixing problem of EMD .Compared with ensemble EMD（EEMD） ,which was by adding white noise before ensemble and averaging ,PEEMD fulfilled this aim by defining the least extrema scale（LES） to measure other extre-ma scales and by adding pseudo-extrema to homogenize the scale distribution .EMD and PEEMD are introduced herein firstly and then PEEMD was compared with EMD and EEMD by analyzing simula-tion signals .Also ,PEEMD was applied to diagnose rotor rubbing faults ,and the results indicate that PEEMD is more accurate in components and more efficient in inhibition of mode mixing than that of EM D and EEM D .
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Generalized empirical mode decomposition and its applications to rolling element bearing fault diagn
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