基于合成谱峭度优化VMD的滚动轴承故障特征提取
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北京建筑大学

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TH165+.3

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Fault feature extraction of rolling bearing based on synthetic spectral kurtosis optimization VMD
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    摘要:

    针对滚动轴承振动信号特征在强噪声的情况下难以提取的问题,提出了一种基于合成谱峭度优化变分模态分解(variational modal decomposition,VMD)的方法。首先,对原始故障信号进行变分模态分解,依据合成谱峭度值最大的原则分别优化VMD的关键参数—模态数和惩罚因子,得到若干本征模态分量(intrinsic mode component,IMF)。然后,计算各IMF峭度,选取峭度值最大的分量作为最优IMF,最后,对最优本征模态分量进行希尔伯特变换,以获得其包络谱,从而实现故障特征频率的提取。通过公开数据集和自制试验台相关数据的分析,表明所提方法能在强噪声背景下有效提取故障信号的故障特征,实现故障类型的判别。

    Abstract:

    In response to the problem that rolling bearing vibration signal characteristics were difficult to be extracted in the case of strong noise, a method based on synthetic spectral cliff to optimise the variational modal decomposition (VMD) was proposed. First, the original fault signal was subjected to variational modal decomposition, and several intrinsic mode components (IMFs) were acquired by optimizing the key parameters of VMD-modal number and penalty factor respectively with the principle of the maximum value of synthetic spectral cliff. Then, the crag of each IMF was calculated, and the component with the maximum crag value was selected as the optimal IMF. Finally, the Hilbert transform was performed on the optimal intrinsic modal components to obtain their envelope spectra, so as to realize the extraction of the fault eigenfrequency. Through the analysis of the public dataset and the relevant data of the homemade test bed, it is shown that the proposed method can effectively extract the fault characteristics of the fault signal under the background of strong noise and realize the discrimination of the fault type.

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  • 收稿日期:2024-04-07
  • 最后修改日期:2024-05-22
  • 录用日期:2024-05-22
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