基于优化VMD和MCKD的滚动轴承早期故障诊断方法

针对滚动轴承早期故障信号易受噪声等背景信息干扰难于提取故障特征的现象，提出了将优化K值的变分模态分解(VMD)和粒子群优化算法(PSO)优化参数L，M的最大相关峭度解卷积(MCKD)相结合提取滚动轴承故障特征频率的方法.首先，确定VMD中K值，对信号进行分解后得到一系列模态分量;然后利用EWK指标选择包含故障信息最多的有效模态分量进行后续分析，利用优化的MCKD对其进行增强;最后对增强信号进行包络解调提取故障特征频率，验证所提方法的有效性.仿真和实验表明该方法可以精确地提取出轴承故障信号中的特征频率，实现故障诊断.

Early Fault Diagnosis Method of Rolling Bearings Based on Optimization of VMD and MCKD

Considering the phenomenon that the early fault signals of rolling bearings are easily interfered by noise and background information and it is difficult to extract fault characteristics， a method combining K value optimization of variational mode decomposition (VMD) and particle swarm optimization (PSO) optimizing the maximum correlated kurtosis deconvolution (MCKD) parameters L， M was proposed to extract the fault characteristic frequency of rolling bearings. Firstly， the K value in VMD was calculated， and the signal was decomposed to obtain a series of modal components. EWK index was used to select the effective modal components that contain the most fault information for subsequent analysis and the optimized MCKD was used to enhance it. Finally， the enhanced signal was subjected to envelope demodulation to extract the fault characteristic frequency， which verified the effectiveness of the proposed method. Simulations and experiments showed that this method can accurately extract the fault characteristic frequency of the signal and realize fault diagnosis.