Yi Cai, Wang Dong, Fan Wei, Tsui Kwok-Leung, Lin Jianhui
School of Automobile and Transportation, Xihua University, Chengdu 610039, China.
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, China.
Sensors (Basel). 2018 Feb 27;18(3):704. doi: 10.3390/s18030704.
Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions.
铁路车轴轴承是车辆中使用的最重要部件之一，其故障可能导致意外事故和经济损失。为实现铁路车轴轴承的状态监测与故障诊断方案，分别提出了时域、频域和形状域中的三个无量纲稳定性指标，以测量轴承振动信号的稳态。首先，利用总体经验模态分解（EEMD）对从一些设计实验中采集的振动数据进行预处理。然后，引入变异系数，分别从预处理后的时域和频域振动数据中构建两个稳态指标。使用形状函数在形状域中构建一个稳态指标。最后，为区分正常和异常的轴承健康状态，提出了一些指导阈值。此外，为识别具有外圈缺陷、滚针缺陷、保持架缺陷和耦合缺陷的车轴轴承，通过实验确定了所有稳定性指标的边界。实验结果表明，所提出的状态监测与故障诊断方案在识别不同轴承健康状况方面是有效的。
