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

发布时间：2018-03-01 02:31

  本文关键词： 小波相邻系数降噪 滚动轴承 时频小波切片变换(FSWT) 早期微弱故障 特征提取　出处：《航空动力学报》2017年05期 　论文类型：期刊论文

【摘要】：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.
[Abstract]:Wavelet adjacent coefficient denoising and time-frequency wavelet slice transform (FSWT) are combined to extract the time-frequency feature of early weak fault of rolling bearing. The vibration data of early weak fault in rolling bearing accelerated fatigue test are analyzed. The results show that the wavelet adjacent coefficients can effectively reduce the background noise of the weak fault characteristics of the submerged rolling bearings, and the time-frequency wavelet slice transform can effectively extract the time-frequency features of the vibration signals after the noise reduction by the wavelet adjacent coefficients. That is, the characteristic frequency and harmonic frequency components of the rolling bearing at the time of failure verify the validity of the method. In addition, by comparing with the results of spectral kurtosis time-frequency analysis, It is proved that the method can accurately capture the time-frequency characteristics of the rolling bearing in the early stage of weak fault, and the superiority of the method is highlighted.
【作者单位】： 郑州轻工业学院机电工程学院;
【基金】：国家青年自然科学基金(51405453,51205371) 郑州轻工业学院博士科研基金
【分类号】：TH133.33
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本文编号：1550038