基于改进VMD和Hilbert包络谱分析的滚动轴承故障诊断方法研究

Research on Fault Diagnosis Method of Rolling Bearing Based on Improved VMD and Hilbert Envelope Spectrum Analysis

在工业生产中,旋转机械设备振动情况比较复杂,在强噪声的情况下,针对非平稳非线性的故障信号特征难以提取的问题,将改进变分模态分解(VMD)和希尔伯特-黄变换(HHT)进行结合的诊断方法用于轴承故障诊断.该方法首先利用模态分量瞬时频率的均值优化VMD参数,得到最佳分解层数K,使用优化参数对采集的信号进行VMD处理,再利用误差能量算法选择合适的有效模态分量(IMF),最后使用希尔伯特变换对选择的最优IMF分量做包络谱,提取出有效故障特征进行故障诊断.使用Hilbert包络谱、经验模态分解(EMD)、聚合经验模态分解(EEMD)等方法进行对比,试验结果表明,该方法能有效提取故障特征.

In industrial production,the vibration of rotating mechanical equipment is relatively complex.In the case of strong noise,for the problem of non-stationary and nonlinear fault signal characteristics that are difficult to extract,the diagnostic method in combination with the improved Variational Mode Decomposition(VMD)and Hilbert-Huang Transform is used to do the bearing fault diagnosis.In this method,the mean value of the instantaneous frequencies of the modal components is used to optimize the VMD parameters to obtain the optimal decomposition level K.The optimized parameters are used to perform the VMD decomposition on the collected signal,and then the error energy algorithm is used to select the appropriate effective modal component(IMF),and finally the Hilbert Transform is used to do the envelope spectrum by selecting the optimal IMF component,and extract effective fault features for fault diagnosis.Using Hilbert envelope spectrum,Empirical Mode Decomposition(EMD),Ensemble Empirical Mode Decomposition(EEMD)and other methods for comparison,the experimental results show that this method can effectively extract fault features.