基于MED和变分模态分解的滚动轴承早期故障诊断方法
发布时间：2018-10-11 10:40
【摘要】：针对滚动轴承早期微弱故障特征容易淹没于环境噪声中而难以提取的问题,提出了最小熵解卷积(MED)降噪和变分模态分解(VMD)相结合的滚动轴承早期故障诊断方法。首先以峭度最大为准则利用MED对轴承振动信号进行降噪处理,然后采用新的高精度多分量信号分解方法——VMD将降噪信号分解为若干个分量,最后通过分析最大峭度分量包络谱中故障频率成分诊断轴承故障。轴承实验分析结果表明了该方法的有效性。
[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.
【作者单位】： 华北电力大学机械工程系;
【基金】：河北省自然科学基金(E2014502052) 中央高校基本科研业务费专项资金(2017MS190,2014MS156,2015XS120)
【分类号】：TH133.33
本文编号：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.
【作者单位】： 华北电力大学机械工程系;
【基金】：河北省自然科学基金(E2014502052) 中央高校基本科研业务费专项资金(2017MS190,2014MS156,2015XS120)
【分类号】：TH133.33
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1 刘志川;唐力伟;曹立军;;基于MED及FSK的滚动轴承微弱故障特征提取[J];振动与冲击;2014年14期
，本文编号：2263896
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