基于集合经验模态分解和小波阈值的真空泵振动信号降噪方法

真空泵的振动信号具有非平稳、非线性的特性，且夹杂着大量背景噪声，难以直接对其特征信号进行提取、分析，阻碍对真空泵的在线故障诊断。为此，文章提出基于集合经验模态分解（EEMD）的真空泵振动信号小波阈值降噪方法：首先将振动信号进行EEMD分解，得到若干个本征模态函数（IMF）与余项，然后引入归一化自相关函数对IMF分量进行筛选，再对筛选出的IMF分量进行小波阈值降噪处理，最后将降噪后的IMF分量与未处理的IMF分量和余项进行重构，得到降噪后的真空泵振动信号。对仿真与实验信号进行降噪处理的结果表明该方法优于现有的降噪方法，为真空泵振动信号的降噪提供了 新的途径。

De-noising based on EEMD and wavelet threshold for vacuum pump vibration signals

The vibration signals of the vacuum pump have non-stationary and non-linear characteristics, mixed with a large number of ambient noise signals. It is difficult to extract and analyze the characteristic signals directly, as a challenge for the online fault diagnosing of the vacuum pump. To deal with that problem, a wavelet threshold de-noising method is proposed based on the ensemble empirical mode decomposition (EEMD) for the vacuum pump vibration signals. Firstly, the vibration signal is decomposed into several intrinsic modal functions (IMF), with a residual by the EEMD. Then, the normalized autocorrelation function is introduced to screen the IMF components, and the filtered IMF components are subjected to the wavelet threshold de-noising. Finally, the de-noised IMF components are reconstructed with the unprocessed IMF components and the residual to obtain the de-noised vibration signal of the vacuum pump. The noise reduction result of the simulation signals and the experimental signals shows that the proposed method is superior to the existing methods with a raised SNR and a diminished RMSE, which provides a new way for de-noising the vacuum pump vibration signals.