基于小波包降噪与VMD的滚动轴承故障特征提取方法

The Rolling Bearing Fault Feature Extraction Method Based on Wavelet Packet Noise Reduction and VMD

摘要: 针对滚动轴承振动信号掺杂有大量随机噪声且自身的非线性非平稳性，致使振动信号中故障特征难以提取的问题，提出基于小波包降噪与VMD的滚动轴承故障特征提取方法。该方法首先将采集到的滚动轴承故障振动信号经过小波包降噪处理，滤除信号中的噪声成分；后利用VMD方法对降噪后信号进行分解，将信号中的故障成分与原有信号成分剥离；最后对分解后所得与原信号有最大相关性的分量进行Hilbert解调，从而提取出故障特征。实验结果表明，基于小波包降噪与VMD的滚动轴承故障特征提取方法能够有效提取出故障的特征成分，实现滚动轴承的故障诊断。

Abstract: Aiming at the problem that a large amount of random noise mixed in the rolling bearing vibration signals and its nonlinear non-stationary, and that the fault features of vibration signal are difficult to extract, the noise reduction based on wavelet packet and VMD rolling bearing fault feature ex-traction method was proposed. Firstly, this method uses the wavelet packet de-noising to process the signal to filter out the noise in the signal component; then, the VMD method is used to decom-pose the signal after noise reduction; finally, the obtained with the original signal have the largest correlation components for the Hilbert demodulation, so as to extract the fault feature. Finally, the IMF that has the largest correlation with original signal was analyzed by Hilbert envelope, so as to extract the fault feature. Experimental results show that the rolling bearing fault feature extraction method based on Wavelet Packet noise reduction and VMD can effectively extract the fault feature and realize the fault diagnosis of rolling bearing.