基于SVMD与参数优化MCKD的轴承故障诊断

Bearing fault diagnosis based on SVMD and parameter optimized MCKD

针对轴承故障信号存在噪声干扰,难以提取故障特征的问题,提出了一种将连续变分模态分解(SVMD)与改进的最大相关峭度反卷积(MCKD)相结合的轴承故障诊断方法。首先,为了表征轴承振动信号中的故障特征,将峭度与高斯核相结合,提出了比峭度指标更为突出的加权峭度指标;其次,利用SVMD方法对轴承信号进行了分解,获得了若干模态分量,并使用加权峭度指标从多个模态分量中筛选出了故障特征最丰富的模态分量;然后,以包络熵为标准,通过几何平均优化器(GMO)优化MCKD的滤波器长度和周期两个参数,获得了最佳的参数组合;最后,采用GMO-MCKD方法对轴承信号进行了降噪,对降噪后的信号进行了包络分析,提取了轴承特征频率;同时,采用粒子群优化(PSO)的变分模态分解(VMD)和粒子群优化的变分模态提取(VME),对轴承信号进行了对照分析。研究结果表明:采用SVMD-GMO-MCKD方法在辛辛那提数据集中诊断出轴承特征频率为234.4 Hz及其二倍频;在西储大学轴承数据集中诊断出轴承特征频率为108.96 Hz,二倍频为218.09 Hz。该方法可以增强滚动轴承的周期性冲击成分,在有干扰的背景下有效地提取出滚动轴承内圈和外圈的故障特征,且轴承故障特征提取效果优于PSO-VMD和PSO-VME方法。

Aiming at the problem that it was difficult to extract fault features from bearing fault signals with noise interference,a bearing fault diagnosis method combining the successive variational modal decomposition(SVMD)with the improved maximum correlation kurtosis deconvolution(MCKD)was proposed.Firstly,in order to characterize the faults in the bearing vibration signals,a weighted kurtosis index,which was more prominent than the kurtosis index,was proposed by combining the kurtosis with the Gaussian kernel.Secondly,the bearing signal was decomposed using the SVMD method to obtain a number of mode components,and the weighted kurtosis index was used to filter out the mode component with the richest fault features from the multiple mode components.Then,using envelope entropy as the standard,the geometric mean optimizer(GMO)was used to optimize the filter length and period of MCKD to obtain the optimal parameter combination.Finally,the GMO-MCKD method was used to reduce the noise of the bearing signals,and the noise-reduced signals were subjected to the envelope analysis to extract the bearing eigenfrequencies.At the same time,the variational mode decomposition(VMD)of particle swarm optimization(PSO)and variational mode extraction(VME)of particle swarm optimization were used for the comparison analysis of the bearing signals.The research results show that the SVMD-GMO-MCKD method diagnose a bearing characteristic frequency of 234.4 Hz and its second harmonic in the Cincinnati dataset,the bearing characteristic frequency diagnosed in the bearing dataset of Western Reserve University is 108.96 Hz and the second harmonic frequency is 218.09 Hz.The method can enhance the periodic shock component of the rolling bearing,effectively extract fault features of inner and outer rings of rolling bearings in the interference background,and the extraction effect of bearing fault features is superior to PSO-VMD and PSO-VME methods.