基于EEMD和参数自适应VMD的高速列车轮对轴承故障诊断

针对高速列车轮对轴承工作环境复杂,振动信号中时常伴有冲击性噪声和循环平稳性噪声,使得传统的参数自适应变分模态分解(variational modal decomposition,VMD)方法对轮对轴承的故障特征信息提取不准确的问题,提出了一种基于集成经验模态分解(ensemble empirical mode deco...

Fault diagnosis of wheelset bearing of high-speed train based on EEMD and parameter adaptive VMD

Here,aiming at the problem of traditional parameter adaptive VMD method being unable to correctly extract fault feature information of wheelset bearing due to complex working environment of wheelset bearing of high-speed train and vibration signals often being accompanied by impact noise and cyclo-stationary noise,an improved parameter adaptive VMD method based on the ensemble empirical mode decomposition(EEMD)preprocessing was proposed.Firstly,EEMD was used to decompose the collected vibration signal,calculate envelope kurtosis values of the original signal and its each component,and select the component whose kurtosis value is larger than the kurtosis value of the original signal for reconstruction to generate a new vibration signal.Then,taking the local maximum envelope spectral kurtosis as the objective function,the new signal was analyzed by using the parameter adaptive VMD method based on particle swarm optimization(PSO)to determine its optimal parameters.Finally,the optimized VMD was used for the decomposition of the new signal,and the component with the maximum envelope spectral kurtosis was selected for envelope demodulation analysis.Through simulation and test data analysis,it was shown that the proposed method can have excellent fault feature extraction effect under strong noise interference;the study results can have a certain theoretical significance and application value for improving fault diagnosis effect of train wheelset bearing.