摘要
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 decomposition,EEMD)预处理的改进参数自适应VMD方法。首先利用EEMD对采集到的振动信号进行分解,计算原始信号以及各分量的包络峭度值,选取峭度值大于原始信号峭度值的分量进行重构,生成新的振动信号;其次以局部最大包络谱峭度为目标函数,利用基于粒子群的参数自适应VMD方法分析新信号,从而确定最佳参数;最后将优化后的VMD用于新信号的分解,选取包络谱峭度值最大的分量进行包络解调分析。通过仿真和试验数据分析,证明了该方法在强噪声干扰下仍具有优良的故障特征提取效果。研究结果对提高列车轮对轴承故障诊断效果有一定的理论意义和应用价值。
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.
作者
李翠省
廖英英
刘永强
LI Cuixing;LIAO Yingying;LIU Yongqiang(School of Traffic and Transportation,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;State Key Laboratary of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第1期68-77,共10页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(11790282,12032017,11802184,11902205,12002221)
河北省科技计划资助项目(20310803D)
河北省自然科学基金资助项目(A2020210028)
石家庄铁道大学研究生创新资助项目(YC2021087)。
关键词
轮对轴承
故障诊断
变分模态分解(VMD)
包络峭度
包络谱峭度
wheelset bearing
fault diagnosis
variational modal decomposition(VMD)
envelope kurtosis
envelope spectral kurtosis
