發(fā)布時間：2018-03-01 02:31

  本文關鍵詞： 小波相鄰系數(shù)降噪 滾動軸承 時頻小波切片變換(FSWT) 早期微弱故障 特征提取　出處：《航空動力學報》2017年05期 　論文類型：期刊論文

【摘要】：將小波相鄰系數(shù)降噪與時頻小波切片變換(FSWT)相結合用于滾動軸承的早期微弱故障時頻特征提取,通過對滾動軸承加速疲勞試驗早期微弱故障振動數(shù)據(jù)進行分析,結果表明:小波相鄰系數(shù)可以有效降低淹沒滾動軸承早期微弱故障特征的背景噪聲;時頻小波切片變換方法能有效提取出經(jīng)小波相鄰系數(shù)降噪后振動信號的時頻特征,即滾動軸承發(fā)生故障時的特征頻率及其諧頻成分,驗證了所述方法的有效性.此外,通過與譜峭度時頻分析結果的對比,證明所述方法更能準確撲捉到滾動軸承發(fā)生早期微弱故障時的時頻特性,突出了所述方法的優(yōu)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.
【作者單位】： 鄭州輕工業(yè)學院機電工程學院;
【基金】：國家青年自然科學基金(51405453,51205371) 鄭州輕工業(yè)學院博士科研基金
【分類號】：TH133.33
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本文編號：1550038