基于频谱分离与小波包分析相结合的滚动轴承故障诊断

Rolling Bearing Fault Diagnosis Based on Spectral Separate and Wavelet Packets Analysis

针对提取的滚动轴承故障振动信号中包含大量噪声,采用频域分离的方法,从故障轴承振动信号中分离出纯故障信号,通过对纯故障信号进行小波包分解和重构,对重构后的小波包系数进行Hilbert包络解调并求取解调后信号的功率谱,从而从功率谱中识别出滚动轴承的故障特征频率,达到滚动轴承故障诊断的目的,并结合实验数据对该方法进行验证,结果证明了该方法的有效性。

In order to resolve the problem of much noise was involved in fault rolling bearing vibration signals,use the method of spectral separating separate pure fault signal from the fault rolling bearing vibration signals,though using wavelet packets decompose and reconstruct the pure fault signal,and using Hilbert transform the wavelet packets coefficients of reconstructed signals,calculating power spectrum density.Search the fault characteristics frequency form the power spectrum density to achieve fault diagnosis.The practice data shows that the method was effective.