本文選題：變分模態(tài)分解　切入點：小波包變換　出處：《圖學學報》2017年02期 　論文類型：期刊論文

【摘要】：針對滾動軸承早期故障振動信號具有能量小、易受背景噪聲干擾,導(dǎo)致故障特征提取困難等問題,提出基于變分模態(tài)分解(VMD)和小波包變換(WPT)相結(jié)合的方法來提取故障特征。首先將振動信號進行VMD分解,得到若干本征模態(tài)分量(IMF);其次,通過峭度準則選取峭度值較大的分量進行重構(gòu);最后將重構(gòu)分量采用WPT方法進行分解,并計算小波包的能量、選取能量集中的頻段進行能量算子解調(diào),從而提取故障特征信息。將該方法應(yīng)用到滾動軸承實測數(shù)據(jù)中,并與目前最常用的方法 EEMD-WPT對特征信號的提取效果作對比。實驗結(jié)果表明該方法可以更精確地提取出的故障特征頻率,驗證了其有效性。
[Abstract]:In view of the problems of low energy and easy to be disturbed by background noise, the early fault vibration signal of rolling bearing is difficult to extract fault features. A method based on variational mode decomposition (VMD) and wavelet packet transform (WPT) is proposed to extract the fault features. Firstly, the vibration signal is decomposed by VMD, and some intrinsic modal components are obtained. Finally, the reconstructed component is decomposed by WPT method, the energy of wavelet packet is calculated, and the frequency band of energy concentration is selected to demodulate the energy operator. In order to extract the fault feature information, the method is applied to the measured data of rolling bearing. The experimental results show that the method can extract the fault feature frequency more accurately and verify its effectiveness.
【作者單位】： 石家莊鐵道大學電氣與電子工程學院;
【基金】：國家自然科學基金項目(11227201,11372199,11572206) 河北省自然科學基金項目(A2014210142)
【分類號】：TH133.33

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本文編號：1601894