基于全矢融合与多维经验模态分解的滚动轴承退化过程频谱结构研究

发布时间：2018-04-27 15:26

本文选题：多维经验模态分解 + 全矢融合　；参考：《中国机械工程》2017年14期

【摘要】：为了识别滚动轴承退化过程,提出一种多维经验模态分解和全矢融合相结合的方法。首先对不同状态的多通道信号同时进行多维经验模态分解,得到一系列多元固有模态函数分量,然后利用互相关系数准则选取最敏感的一阶固有模态函数分量进行全矢包络分析来提取信号的特征。为了验证该方法的有效性,分别对模拟信号和实际信号进行了分析。结果表明此方法在出现故障时,能够很好地表征频谱结构的变化;随着故障严重程度的增加,频谱结构变得复杂,且呈现出了规律性。
[Abstract]:In order to identify the degradation process of rolling bearings, a multi-dimensional empirical mode decomposition and full vector fusion method is proposed. At first, multi-channel signals with different states are decomposed simultaneously by multi-dimensional empirical mode decomposition, and a series of intrinsic modal function components are obtained. Then the most sensitive components of the first order inherent mode function are selected by using the correlation number criterion to extract the characteristics of the signal by the full vector envelope analysis. In order to verify the effectiveness of the method, the analog signal and the actual signal are analyzed respectively. The results show that this method can well characterize the variation of spectrum structure in the event of failure, and with the increase of fault severity, the spectrum structure becomes complex and presents regularity.
【作者单位】：郑州大学振动工程研究所;
【分类号】：TH133.33

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