基于VMD与自适应MOMEDA的回转支承故障诊断

Fault Diagnosis of Slewing Bearing Based on VMD and Adaptive MOMEDA

针对强背景噪声下的低速重载回转支承难以提取故障特征的问题,提出了一种变分模态分解(VMD)与多点最优最小熵解卷积(MOMEDA)相结合的回转支承故障诊断方法。首先,采用VMD算法对原始振动信号进行分解,从中选出峭度最优分量;其次,利用灰狼优化算法(GWO)优化MOMEDA算法中的参数T,再基于优化的MOMEDA算法增强最优分量中的故障冲击成分;最后,对处理后的最优分量进行包络谱分析,提取故障特征。与VMD-MED方法相比,所提方法能够更准确突出信号中的周期性故障冲击成分,有效提取低速重载回转支承故障特征。

Aiming at the problem that it is difficult to extract the fault characteristics of the low-speed and heavy-load slewing bearing under strong background noise,a slewing bearing fault diagnosis method based on the combination of variational mode decomposition and multipoint optimal minimum entropy deconvolution adjusted is proposed.Firstly,the VMD algorithm is used to decompose the original vibration signal,and the optimal component of kurtosis is selected from it.Secondly,the parameter T in the MOMEDA algorithm is optimized by the grey wolf optimization algorithm,and then the fault impact component of the optimal component is enhanced based on the optimized MOMEDA algorithm.Finally,envelope spectrum analysis is performed on the processed optimal component to extract fault characteristics.Compared with the VMD-MED method,the proposed method can more accurately highlight the periodic fault impact components in the signals,and effectively extract the fault features of low-speed and heavy-load slewing bearings.