VMD参数优化及其在轴承故障特征提取中的应用

Optimization of VMD Parameters and Its Application in Bearing Fault Feature Extraction

针对群优化算法对变分模态分解所需模态数和二次惩罚项参数寻优效率较低的问题,提出了快速局部均值经验模态分解的信号预处理方法.对预处理后的模态分量根据相似系数准则进行模态数预估,同时利用多评价指标选择二次惩罚参数.针对VMD对故障信号中存在的固有振动高频带分解效果较差的问题,利用自相关能量函数实现降噪和减小高频带的影响.通过仿真实验和实测轴承故障数据分析,并与群优化算法选择参数以及中心频率相近选取模态数的VMD分解效果相比,该方法能有效提取故障信号的特征频率.

To solve the problems existing in the swarm optimization algorithm,such as the low efficiency in searching the modal number and quadratic penalty parameters for the variational mode decomposition,a signal pretreatment method was proposed for fast local mean empirical mode decomposition.The number of modal was estimated according to the similarity coefficient criterion,and the penalty parameter was selected with the multi-evaluation factors.In order to reduce the influences of high natural frequencies band on results of VMD,the signal was reconstructed based on autocorrelation energy function to reduce the influence of noise and high frequency band.Simulation test and real data analysis of bearing fault were carried out to compare the parameter selection effect with swarm optimization algorithm and VMD modal selection effect based on similar center frequency.The results show that this method can extract early weak fault characteristic frequencies of bearings effectively.