基于变分模态分解和 Teager 能量算子的滚动轴承故障特征提取

摘要

In order to solve the problems that the fault features of rolling bearings in early failure duration are difficult to extract,an incipient fault diagnosis method for rolling bearings based on variational mode decomposition (VMD)and Teager energy operator was proposed.Firstly,VMD was used to decompose a fault signal into several intrinsic mode functions (IMFs),and then the IMF with the biggest kurtosis was selected with the kurtosis criterion and demodulated into Teager energy spectrum with Teager energy operator.The proposed method was applied in simulated fault signals and actual fault signals of rolling bearings.The results showed that this method can improve the efficiency of signal decomposition and reduce the effect of noise to realize accurate diagnosis of rolling bearings'faults,the effectiveness of the proposed method is verified.%针对滚动轴承早期故障振动信号信噪比低、故障特征提取困难的问题，提出了基于变分模态分解和能量算子的滚动轴承故障特征提取方法。该方法首先对故障信号进行变模态分解（Variational Mode Decomposition，VMD），得到若干本征模态分量（Intrinsic Mode Function，IMF）；其次，通过峭度准则选取其中峭度最大的分量进行 Teager 能量算子解调，得到信号的 Teager 能量谱。将该方法应用到滚动轴承仿真故障数据和实际数据中，结果表明，该方法提高了信号的分解效率，降低了噪声的影响，能够实现滚动轴承故障的精确诊断，证明了该方法的有效性。