基于奇异值分解和变分模态分解的轴承故障特征提取

Fault feature extraction of bearing faults based on singular value decomposition and variational modal decomposition

为了有效提取轴承故障,提出了基于变分模态分解和奇异值分解降噪的故障特征提取方法。通过对故障信号进行变分模态分解,获得其本征模态函数。基于峭度指标,选择包含故障信息的本征模态函数进行信号重构。利用奇异值分解降噪技术对重构信号进行处理,提高信噪比。最后对降噪信号进行包络解调提取故障特征频率。与常见的故障特征提取方法相比,该方法能有效辨别滚动轴承的典型故障,突出故障特征,提高滚动轴承的故障诊断效果。

In order to extract fault features of rolling bearings effectively,a method based on variational mode decomposition and singular value decomposition was proposed.The Intrinsic Mode Function （IMF）was obtained by variational mode decomposition.The IMF containing fault information was selected to reconstruct the signal according to the index of kurtosis.The singular value decomposition was used to reduce noise and increase the ratio of signal-to-noise. Then the fault features were extracted by using envelope spectrum analysis.Compared with common fault features extraction methods,the proposed method can distinguish typical faults,highlight fault features and improve diagnostic effect.