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Leaves on the Line: Low Adhesion Detection in Railways

机译:离开线上:铁路中的低附着力检测

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Abstract: Regions of extreme low-adhesion between the wheel and rail can cause critical problems in traction and braking. This can manifest in operational issues such as signals being passed at danger, or pessimistic network wide responses to mitigate for localised issues. Poor traction conditions can be caused by oil contaminants, rain, ice, condensation of water droplets (micro-wetting) or leaves on the line, where compressed leaf contamination can cause a rapid decrease in adhesion. The complexity of the problem arises as a result of the inability to directly measure and monitor all the factors involved. There remains a lack of real-time information regarding the state and location of low-adhesion areas across rail networks. On-board low adhesion detection technology installed to in-service vehicles is a suggested method to capture up-to-date adhesion information network wide and minimise significant disruptions and cancellations in railway schedules. This paper extends a principle of a model-based estimation technique previously developed in straight track running for operating in a curving scenario. The vehicle model of focus here will be a simplified single-wheelset model attached to a suspended mass via representative stiffness and damping components. It is shown that in order for instantaneous creep forces to be estimated, the radius of the curve is required.
机译:摘要:轮轨之间的极低附着力区域可能会在牵引和制动方面造成严重问题。这可以表现为操作问题,例如在危险中传递信号,或在网络范围内悲观的响应以减轻局部问题。油污,雨水,冰,水滴的凝结(微湿)或生产线上的叶子可能会导致牵引条件不佳,在这些情况下,压缩的叶子污染物会导致粘附力迅速下降。由于无法直接测量和监视所有涉及的因素,因此导致了问题的复杂性。仍然缺乏有关铁路网络中低粘附力区域的状态和位置的实时信息。建议在行车中安装车载低附着力检测技术,以捕获最新的附着力信息网络,并最大程度地减少铁路调度中的重大干扰和取消,这是建议的方法。本文扩展了先前在直线运行中开发的基于模型的估算技术的原理,用于在弯曲情况下运行。这里关注的车辆模型将是简化的单轮模型,该模型通过代表性的刚度和阻尼分量附加到悬架上。结果表明,为了估计瞬时蠕变力,需要曲线的半径。

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