基于小波相邻系数降噪的滚动轴承早期微弱故障时频特征提取

Time-frequency feature extraction of rolling bearing's early weak fault based on wavelet de-noising using neighboring coefficients

将小波相邻系数降噪与时频小波切片变换(FSWT)相结合用于滚动轴承的早期微弱故障时频特征提取,通过对滚动轴承加速疲劳试验早期微弱故障振动数据进行分析,结果表明:小波相邻系数可以有效降低淹没滚动轴承早期微弱故障特征的背景噪声;时频小波切片变换方法能有效提取出经小波相邻系数降噪后振动信号的时频特征,即滚动轴承发生故障时的特征频率及其谐频成分,验证了所述方法的有效性.此外,通过与谱峭度时频分析结果的对比,证明所述方法更能准确扑捉到滚动轴承发生早期微弱故障时的时频特性,突出了所述方法的优越性.

The wavelet de-noising using neighboring coefficients and frequency slice wavelet transform （FSWT） were combined for weak fault time-frequency feature extraction of rolling bearing.Based on the analysis results of the vibration data of rolling element bearing＇s early weak fault,the strong background noise of rolling bearing can be decreased effectively by the wavelet de-nosing using neighboring coefficients method.Furthermore,the denoised signal was handled by the FSWT method and better time-frequency feature extraction result was obtained compared with the method using FSWT directly,so the effectiveness of the proposed method was verified.Besides,the advantages of the proposed method were also verified by comparing with other time-frequency method such as spectral kurtosis.