轴承早期故障特征提取方法研究

针对滚动轴承早期故障信号被背景噪声淹没、故障特征不明显的问题,提出一种基于小波包分解和互补集合经验模态分解(CEEMD)的轴承早期故障信号特征提取方法.利用Matlab软件对采集到的轴承振动信号进行快速谱峭度分析,根据峭度最大化原则确定带通滤波器的中心频率和带宽,设计带通滤波器;对经过带通滤波器滤波后的信号进行小波包分解和CEEMD分解,根据峭度、相关系数筛选出有效本征模态函数(IMF)分量;利用IMF分量重构小波包信号,对重构小波包信号进行包络谱分析,提取轴承早期故障信号特征频率.该方法通过谱峭度分析降低背景噪声干扰,通过小波包分解增强故障冲击信号,并将CEEMD与小波包分解相结合,解决经典EMD分解存在的模态混叠、无效分量问题.仿真结果表明,相较于传统包络解调算法,重构后信号的背景噪声得到抑制,故障特征分量突出,验证了所提方法的可行性和有效性.

Research on a bearing early fault features extraction method

In view of problems that early fault signals of rolling bearings are submerged by background noise and fault characteristics are not obvious, a bearing early fault feature extraction method based on wavelet packet decomposition and CEEMD was proposed. Matlab software is used to perform rapid spectral kurtosis analysis on the collected vibration signals, and the center frequency and bandwidth of the band-pass filter is determined according to maximum kurtosis principle and used to design the band-pass filter. Wavelet packet decomposition and CEEMD decomposition are perform to the signal filtered by the band-pass filter, and effective intrinsic modal function( IMF)components are selected according to the kurtosis and correlation coefficient and used to reconstruct the wavelet packet signal. Characteristic frequency of bearing early fault signal is extracted by envelope spectrum analysis of the reconstructed wavelet packet signal. The method reduces background noise interference through spectral kurtosis analysis, enhances the fault impact signal through wavelet packet decomposition, and combines CEEMD with wavelet packet decomposition to solve the problem of modal aliasing and invalid components in classical EMD decomposition. The simulation results show that compared with traditional envelope demodulation algorithm, the background noise of the reconstructed signal is suppressed and the fault feature component is prominent, which verifies the feasibility and effectiveness of the proposed method.