基于VMD-WPT和能量算子解调的滚动轴承故障诊断研究

Rolling Bearing Fault Feature Extraction Based on Variational Mode Decomposition-Wavelet Packet Transform and Energy Operator Demodulation

针对滚动轴承早期故障振动信号具有能量小、易受背景噪声干扰,导致故障特征提取困难等问题,提出基于变分模态分解(VMD)和小波包变换(WPT)相结合的方法来提取故障特征。首先将振动信号进行VMD分解,得到若干本征模态分量(IMF);其次,通过峭度准则选取峭度值较大的分量进行重构;最后将重构分量采用WPT方法进行分解,并计算小波包的能量、选取能量集中的频段进行能量算子解调,从而提取故障特征信息。将该方法应用到滚动轴承实测数据中,并与目前最常用的方法 EEMD-WPT对特征信号的提取效果作对比。实验结果表明该方法可以更精确地提取出的故障特征频率,验证了其有效性。

In order to solve the problems that the fault feature of rolling bearing in early failure period is difficult to extract,an incipient fault diagnosis method for rolling bearing based on variational mode decomposition(VMD)and wavelet packet transform(WPT)was proposed.The variational mode decomposition was firstly used to decompose the multi-component signal into a number of intrinsic mode functions(IMF),and then the IMFS of the maximum kurtosis were selected to form the new information based on Kurtosis Criterion.Finally,the new signal was decomposed and reconstructed by adopting wavelet packet transform,after that,the energy of every frequency band was calculated,and the frequency band with the maximal signal was chosen and demodulated into energy spectrum with Teager energy operator demodulation method.In order to verify the effectiveness of the proposed method,practical engineering experiments had been carried out and the effect was compared with the EEMD-WPT method for rolling bearing inner fault signals.The results show that compared with the other method,the proposed method can not only reduces the effect of noise but also implement accurate diagnosis.