Increasing service time makes the axlebox bearing of railway vehicle vulnerable to develop a fault in inner or outer races, which can cause some serious adverse effects on a railway vehicle’s safe operation. To tackle this problem, we established a railway vehicle vertical-longitudinal dynamic model with inner/outer races faults of axlebox bearing and validated it by experimental data. We utilized the time-synchronous average (TSA) technology to filter the raw signals and studied their vibration features. The results show that the longitudinal vibration features are more sensitive for inner race fault identification, while the vertical vibration features are more suitable for outer race fault identification. For inner race fault identification, the indicator peak-to-peak value (PPV) that increases 1056% relative to the healthy state at the most severe fault performs the best sensitivity. For outer race fault identification, the indicator skewness value (SV) that increases 518% relative to the healthy state at the most severe fault exhibits the best performance. The research work can provide meaningful guidance for accurate diagnosis of axlebox bearing faults of railway vehicles.

使用寿命的增加使铁路车辆的轴箱轴承很容易在内圈或外圈出现故障，从而可能对铁路车辆的安全运行造成严重的不利影响。为了解决这个问题，我们建立了具有轴箱轴承内/外圈故障的铁路车辆纵向-纵向动态模型，并通过实验数据对其进行了验证。我们利用时间同步平均（TSA）技术来过滤原始信号并研究其振动特性。结果表明，纵向振动特征对内圈故障的识别更加敏感，而纵向振动特征更适合外圈故障的识别。为了识别内圈故障，在最严重的故障下，相对于健康状态增加1056％的指标峰峰值（PPV）表现出最佳的灵敏度。对于外圈故障识别，相对于最严重故障下的健康状态增加518％的指示器偏度值（SV）表现出最佳性能。该研究为铁路车辆轴箱轴承故障的准确诊断提供了有意义的指导。