关键词: 变分模态分解, 最小熵解卷积, 轴承故障, 包络解调

Abstract: In the strong noise environment,the fault feature information of rolling bearing is very weak and difficult to be extracted,propose the Variational Modal Decomposition (VMD) and the Minimum Entropy Deconvolution (MED) method to extract fault feature of rolling bearing.Firstly,VMD was used to decompose original fault signals into several components.Due to influence of noise,it is difficult to extract the effective fault feature information from the component.According to the correlation coefficient criterion,selecting the bigger correlation coefficient of component with the original signal to reconstruct,and then the reconstructed signal is processed by the Minimum Entropy Deconvolution(MED).Finally,the processed signal is analyzed by Hilbert envelope.The fault characteristic frequency can be extracted accurately from the envelope spectrum through the analysis of the experimental data,the results show that the method can effectively reduce the influence of the noise,and accurately realize the extraction of bearing fault feature information.

Key words: Variational Mode Decomposition(VMD), Minimum Entropy Deconvolution(MED), bearing fault, envelope demodulation