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中文摘要：
作为对经验模态分解（EMD）的改进,局部特征尺度分解（LCD）也有类似EMD的模态混淆问题。基于噪声辅助分析的总体平均经验模态分解（EEMD）和完备的EEMD（CEEMD）等是抑制分解模态混淆的有效途径。然而此类方法伪分量较多、得到的分量未必满足IMF分量定义等。针对此,提出了一种完备的总体平均局部特征尺度分解（CELCD）,并通过仿真信号将CELCD方法与CEEMD进行了对比,结果表明CELCD能够有效抑制LCD模态混淆,而且在抑制伪分量的产生,提高正交性和分量的精确性等方面具有一定的优越性。最后论文将CELCD方法应用于转子碰摩故障的诊断,结果表明了方法的有效性。
英文摘要：
As an improvement version of empirical mode decomposition （EMD）, local characteristic-scale decomposition （LCD） also has the problem of mode mixing. The noise-assisted methods, such as ensemble EMD （EEMD） and complete EEMD （CEEMD）, are effective ways to restrain the mode mixing. However, pseudo-components are easily generated by them and the obtained components sometimes do not meet the definition of IMF. A novel signal processing method called complete ensemble LCD （CELCD） is proposed to resolve mode mixing involved in LCD. Also, the comparisons between CELCD and CEEMD are studied by analyzing simulation signal, and the results indicate that CELCD could effectively restrain the mode mixing of LCD and generate more precise components. The rotor rubbing fault diagnosis verifies the validity of the proposed method.
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Generalized empirical mode decomposition and its applications to rolling element bearing fault diagn
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