基于自适应多尺度形态学分析的车轮扁疤故障诊断方法

建立了56自由度车辆动力学模型与车轮扁疤模型, 计算了车辆的动态响应。车辆的振动信息往往受到轨道不平顺和车速波动等因素的影响, 为了能在强噪声背景下有效提取轮轨冲击特征, 提出了自适应多尺度形态学滤波分析方法, 研究了车轮扁疤引起的轴箱振动特征, 分析了轨道激扰和车辆运行速度对车轮扁疤故障诊断效果的影响。仿真结果表明: 在100、150、200km·h^-1的车速和美国五级谱、三级谱的激扰下, 分别使用7个和9个尺度的结构元素进行形态学滤波, 正确地识别出10、15、20Hz车轮扁疤故障频率。实测结果表明: 当车速为40km·h^-1时, 使用7个尺度的结构元素进行形态学滤波, 提取出了2 Hz的故障频率, 此频率与理论故障频率相对应, 诊断结果可靠。

Fault diagnosis method of railway vehicle with wheel flat based on self-adaptive multi-scale morphology analysis

A vehicle system dynamics model with 56 degrees of freedom and a wheel flat model were set up to calculate railway vehicle dynamic responses.The vibration information of vehicle was often influenced by various interferences, such as track irregularity and vehicle speed alteration.In order to effectively extract the wheel-track impact features from strong background noises, a self-adaptive multi-scale morphology filtering analysis algorithm was proposed to study the axle box vibration characteristics caused by wheel flat.The influences of track irregularity and vehicle running speed on the fault diagnosis result of axle box were discussed.Simulation result shows that the fault frequencies of 10, 15, 20 Hz are obtained by using morphology filter based on 7-scale and 9-scale structural elements at the speeds of 100, 150, 200km·h-1 with the American fifth grade and third grade track irregularities.Test result demonstrates that the fault frequency of 2 Hz is obtained by using morphology filter based on 7-scale structural element atthe speed of 40km·h-1, which is corresponding to the theoretic frequency of wheel flat, so diagnosis result is reliable.