基于优化VMD-MCKD和谱峭度的滚动轴承复合故障诊断

Composite Fault Diagnosis of Rolling Bearing Based on Optimized VMD-MCKD and Spectral Kurtosis

针对滚动轴承振动信号中复合故障特征难以准确提取而导致故障诊断困难的问题,提出一种基于优化变分模态分解(VMD)和最大相关峭度解卷积(MCKD)结合快速谱峭度算法的滚动轴承复合故障诊断方法。利用改进麻雀搜索算法(ISSA)优化VMD和MCKD的参数,使用优化后的VMD对复合故障信号进行分解,并根据峭度准则筛选有效本征模态函数(IMF)进行信号重构,使用优化后的MCKD对重构信号进行解卷积与故障特征增强,并对解卷积信号进行包络谱分析提取故障特征频率。利用快速谱峭度算法对未提取出故障特征频率的解卷积信号进行处理,得到故障信息最丰富的频带参数并进行带通滤波处理。最后,对滤波后的信号进行包络谱分析,提取故障特征频率,从而实现故障诊断。仿真及实验结果表明:所提方法能有效分离复合故障并提取出故障特征频率,有效实现了复合故障诊断。

Aiming at the difficulty of fault diagnosis due to the difficulty of extracting complex fault features from the vibration signals of rolling bearings,a novel method for complex fault diagnosis of rolling bearings based on optimal variable mode decomposition(VMD) and maximum correlation kurtosis deconvolution(MCKD) combined with fast spectral kurtosis algorithm was proposed.The parameters of VMD and MCKD were optimized using the improved sparrow search algorithm(ISSA),and the compound fault signal was decomposed using the optimized VMD and the effective intrinsic mode functions(IMF) were screened according to the kurtotic criterion for signal reconstruction.The optimized MCKD was used to deconvolve the reconstructed signal and enhance the fault feature.The deconvolved signal was analyzed by envelope spectrum to extract the fault feature frequency.The fast spectral kurtosis algorithm was used to process the deconvolution signal without fault characteristic frequency extracted to obtain the frequency band parameters with the most abundant fault information and perform band-pass filtering.Finally,the envelope spectrum of the filtered signal was analyzed,and the fault characteristic frequency was extracted to realize the fault diagnosis.The simulation and experimental results show that the proposed method can be used to effectively separate the complex fault and extract the fault characteristic frequency,so the complex fault diagnosis is realized effectively.