| 蔡昕一,马 军,李 祥.改进复合插值包络经验模态分解的
滚动轴承故障特征提取方法[J].电子测量与仪器学报,2023,37(1):191-203 |
| 改进复合插值包络经验模态分解的
滚动轴承故障特征提取方法 |
| Fault feature extraction method of rolling bearing based on the improvedcomposite interpolation envelope empirical mode decomposition |
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| DOI：10.13382/j.issn.1000-7105.2023.01.021 |
| 中文关键词: 改进复合插值 经验模态分解 C-indexTE 复合指标 故障特征提取 |
| 英文关键词:improved composite interpolation empirical mode decomposition C-indexTE composite index fault feature extraction |
| 基金项目:国家自然科学基金(62163020)、云南省基础研究计划项目(202102AD080007)资助 |
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| 中文摘要: |
| 针对复合插值包络经验模态分解(CIEEMD)方法存在非平稳系数阈值无法自适应确定的问题,提出了一种改进复合插
值包络经验模态分解(ICIEEMD)方法。 首先,以边长为 ε 的网格覆盖振动信号求出其分形盒维数,实现信号非平稳阈值自适应
选取,分解得到若干固有模态函数(IMF);其次,结合互相关系数、时域峭度和包络谱峭度建立互相关系数-TE 峭度(C-indexTE)
复合指标,筛选出有效 IMF 分量并重构信号,使用 Teager 能量算子解调获得重构信号的能量谱,实现滚动轴承故障特征提取;
最后,基于仿真信号和实验台滚动轴承数据集进行实验分析,与 CIEEMD 方法和谱峭度法相比,所提方法能够提取出更加清晰
的故障特征频率,证明了所提方法的可行性和有效性。 |
| 英文摘要: |
| Aiming at the problem that the composite interpolation envelope empirical mode decomposition (CIEEMD) method is lack of
self-adaptability in the selection of non-stationary coefficient threshold, an improved composite interpolation envelope empirical mode
decomposition (ICIEEMD) method is proposed. Firstly, the fractal box dimension is calculated from the vibration signal covered by grids
with side length of ε, and the non-stationary threshold is adaptively selected. After decomposition, some intrinsic mode functions (IMF)
are obtained. Secondly, combining with the correlation coefficient, the kurtosises of time domain signal and of envelope spectrum to
establish the composite index of correlation coefficient and TE kurtosises (C-indexTE), then the effective IMF components were selected
and reconstructed into a new signal. The energy spectrum of the reconstructed signal is obtained by using Teager energy operator, and the
fault feature extraction of rolling bearing is realized. Finally, based on the simulation signal and the experimental data set of rolling
bearing, the experimental analysis is carried out. The proposed method can extract more clear fault feature frequencies than the CIEEMD
and spectral kurtosis methods, which proves the effectiveness and feasibility of the proposed method. |
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