基于MED和變分模態(tài)分解的滾動軸承早期故障診斷方法
發(fā)布時間：2018-10-11 10:40
【摘要】：針對滾動軸承早期微弱故障特征容易淹沒于環(huán)境噪聲中而難以提取的問題,提出了最小熵解卷積(MED)降噪和變分模態(tài)分解(VMD)相結合的滾動軸承早期故障診斷方法。首先以峭度最大為準則利用MED對軸承振動信號進行降噪處理,然后采用新的高精度多分量信號分解方法——VMD將降噪信號分解為若干個分量,最后通過分析最大峭度分量包絡譜中故障頻率成分診斷軸承故障。軸承實驗分析結果表明了該方法的有效性。
[Abstract]:Aiming at the problem that the early weak fault features of rolling bearings are easily submerged in ambient noise and difficult to extract, a new method of early fault diagnosis of rolling bearings is proposed, which combines minimum entropy deconvolution (MED) noise reduction with variational mode decomposition (VMD). Firstly, the maximum kurtosis is taken as the criterion to reduce the noise of bearing vibration signal by using MED, and then a new high-precision multi-component signal decomposition method, VMD, is used to decompose the noise reduction signal into several components. Finally, the fault frequency component in the envelope spectrum of the maximum kurtosis component is analyzed to diagnose the bearing fault. The experimental results show that the method is effective.
【作者單位】： 華北電力大學機械工程系;
【基金】：河北省自然科學基金(E2014502052) 中央高�；究蒲袠I(yè)務費專項資金(2017MS190,2014MS156,2015XS120)
【分類號】：TH133.33
本文編號：2263896
[Abstract]:Aiming at the problem that the early weak fault features of rolling bearings are easily submerged in ambient noise and difficult to extract, a new method of early fault diagnosis of rolling bearings is proposed, which combines minimum entropy deconvolution (MED) noise reduction with variational mode decomposition (VMD). Firstly, the maximum kurtosis is taken as the criterion to reduce the noise of bearing vibration signal by using MED, and then a new high-precision multi-component signal decomposition method, VMD, is used to decompose the noise reduction signal into several components. Finally, the fault frequency component in the envelope spectrum of the maximum kurtosis component is analyzed to diagnose the bearing fault. The experimental results show that the method is effective.
【作者單位】： 華北電力大學機械工程系;
【基金】：河北省自然科學基金(E2014502052) 中央高�；究蒲袠I(yè)務費專項資金(2017MS190,2014MS156,2015XS120)
【分類號】：TH133.33
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1 劉志川;唐力偉;曹立軍;;基于MED及FSK的滾動軸承微弱故障特征提取[J];振動與沖擊;2014年14期
，本文編號：2263896
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