Abstract

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.

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