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A digital filter-based approach to the remote condition monitoring of railway turnouts

机译：基于数字滤波器的铁路道岔远程状态监控方法

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摘要

Railway operations in Europe have changed dramatically since the early 1990s, partly as a result of new European Union Directives. Performance targets have become more and more exacting, due to reductions in state support for railways and the need to increasing traffic. More intensive operations also place greater demands on the hardware of the railway. This is true for both rolling stock and infrastructure subsystems and components, particularly so in the case of the latter where the time available for maintenance is being reduced. The authors of this paper focus on the railway infrastructure, and more specifically on points. These are critical elements whose reliability is key to the operation of the whole system. Using intelligent monitoring systems, it is possible to predict problems and enable quick recovery before component failures disrupt operations. The authors have studied the application of remote condition monitoring to point mechanisms and their operation, and have identified algorithms which may be used to identify incipient failures. In this paper, the authors propose a Kalman filter for the linear discrete data filtering problem encountered when using current sensor data in a point condition monitoring system. The reason for applying Kalman filtering in this study was to increase the reliability of the model presented to the rule-based decision mechanism.
机译：自1990年代初以来，欧洲的铁路运营发生了翻天覆地的变化，部分原因是新颁布的欧盟指令。由于国家对铁路的支持减少以及需要增加交通量，性能目标变得越来越严格。更加密集的运营也对铁路硬件提出了更高的要求。对于机车车辆和基础设施子系统及组件均是如此，尤其是在后者的情况下，可维护时间减少了。本文的作者专注于铁路基础设施，尤其是重点。这些是至关重要的元素，其可靠性是整个系统运行的关键。使用智能监控系统，可以预测问题并在组件故障中断操作之前实现快速恢复。作者研究了远程状态监视在指向机制及其操作方面的应用，并确定了可用于识别初期故障的算法。在本文中，作者针对点状态监测系统中使用电流传感器数据时遇到的线性离散数据滤波问题提出了一种卡尔曼滤波器。在这项研究中应用Kalman滤波的原因是为了提高呈现给基于规则的决策机制的模型的可靠性。

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