基于形态分量分析的高速列车轴箱轴承故障诊断方法

Failure Diagnosis Method for Axle Box Bearing of High-speed Train Based on Morphological Component Analysis

高速列车在运行过程中,由于车轮不圆、钢轨波磨、轨道焊缝等激扰的影响,很容易激起轮轨之间的高频振动。当高速列车轴箱轴承同时出现故障和冲击激扰的情况下,现有的高速列车轴箱轴承故障诊断方法很难从中提取出轴承故障脉冲信号。针对这种情况,文中将形态分量分析应用到轴箱轴承故障特征提取,为了便于将其进一步用于在线监测,对该方法中稀疏表示的字典进行了改进。仿真和试验表明,该方法明显优于经验模态分解方法,能够有效地分离出轴承故障周期性脉冲信号,并能将其他随机冲击剔除。

During the operation of high-speed trains,high-frequency vibration between the wheel and rail is easily aroused by the disturbance of wheel flat,rail corrugation,rail weld and so on.When the axle box bearings of high-speed train have both faults and shock excitation,the existing fault diagnosis method for axle box bearing of high-speed train is difficult to extract the bearing fault pulse signal.In view of this situation,this paper applies the morphological component analysis to the extraction of the fault characteristic signal of the axle box bearing,and in order to facilitate the application to online monitoring,the dictionary of sparse representation in the method is improved.Both simulation and experiment show that the method is superior to the empirical mode decomposition method,which can effectively separate the periodic pulse signal of bearing fault and eliminate other random shocks.