The railway-rolling stock wheel flange lubricator protects the wheels and railhead by lubricating their contacts. Failed or missing flange lubricators can lead to excessive wheel wear, wheel flats, wheel cracks, rolling contact fatigue, rail damage, and derailment accidents. In extreme cases, missing or worn flange lubricators due to nonlinear rail conditions may lead to fire hazards, particularly in underground rail infrastructure. In addition, the location of lubricators present accessibility issues and prolong the diagnosis of failure. This study therefore proposes an adaptive risk-based support vector regression (SVR) machine with a Gaussian kernel function that can accurately and proactively predict the wear loss of flange lubricators from a small data set. While most flange lubricators fail owing to wear loss, others fail owing to premature failure modes such as cracks and fatigue. The risk-informed feature evaluates failure rates associated with failures other than wear loss to support a balanced determination of the optimised replacement frequency. The proposed model was applied and validated as a case study for the London underground train. The findings showed that the optimised maintenance inspection of the flange lubricator, as a balance between safety and organisational resource constraints, was an average of every 4000 km between train operations.

中文翻译：

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用于部件更换的风险信息支持向量机回归模型——以铁路法兰润滑器为例

铁路机车车辆车轮法兰润滑器通过润滑车轮和轨头的触点来保护它们。法兰润滑器失效或缺失会导致车轮过度磨损、车轮扁平、车轮裂纹、滚动接触疲劳、轨道损坏和脱轨事故。在极端情况下，由于非线性轨道条件导致法兰润滑器缺失或磨损可能会导致火灾危险，尤其是在地下铁路基础设施中。此外，润滑器的位置存在可访问性问题并延长故障诊断时间。因此，本研究提出了一种基于风险的自适应支持向量回归 (SVR) 机器，该机器具有高斯核函数，可以根据小数据集准确、主动地预测法兰润滑器的磨损损失。虽然大多数法兰润滑器因磨损损失而失效，其他的失败是由于过早的失效模式，如裂纹和疲劳。风险告知功能评估与磨损以外的故障相关的故障率，以支持对优化更换频率的平衡确定。提出的模型作为伦敦地铁列车的案例研究得到了应用和验证。研究结果表明，作为安全和组织资源限制之间的平衡，法兰润滑器的优化维护检查平均每 4000 公里列车运行一次。提出的模型作为伦敦地铁列车的案例研究得到了应用和验证。研究结果表明，作为安全和组织资源限制之间的平衡，法兰润滑器的优化维护检查平均每 4000 公里列车运行一次。提出的模型作为伦敦地铁列车的案例研究得到了应用和验证。研究结果表明，作为安全和组织资源限制之间的平衡，法兰润滑器的优化维护检查平均每 4000 公里列车运行一次。