Measurements of low-frequency vibration are increasingly being used to assess the condition and performance of railway tracks. Displacements used to characterise the track movement under train loads are commonly obtained from velocity or acceleration signals. Artefacts from signal processing, which lead to a shift in the datum associated with the at-rest position, as well as variability between successive wheels, mean that interpreting measurements is non-trivial. As a result, deflections are often interpreted by inspection rather than following an algorithmic or statistical process. This can limit the amount of data that can be usefully analysed in practice, militating against widespread or long-term use of track vibration measurements for condition or performance monitoring purposes. This paper shows how the cumulative distribution function of the track deflection can be used to identify the at-rest position and to interpret the typical range of track movement from displacement data. This process can be used to correct the shift in the at-rest position in velocity or acceleration data, to determine the proportion of upward and downward movement and to align data from multiple transducers to a common datum for visualising deflection as a function of distance along the track. The technique provides a means of characterising track displacement automatically, which can be used as a measure of system performance. This enables large volumes of track vibration data to be used for condition monitoring.

中文翻译：

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自动处理从速度和加速度测量中获得的铁路轨道偏转信号

低频振动的测量越来越多地用于评估铁路轨道的状况和性能。用于表征列车负载下轨道运动的位移通常是从速度或加速度信号中获得的。来自信号处理的人工制品会导致与静止位置相关的数据发生偏移，以及连续车轮之间的可变性，这意味着解释测量值并非易事。因此，偏差通常通过检查来解释，而不是遵循算法或统计过程。这会限制可在实践中进行有用分析的数据量，从而妨碍广泛或长期使用轨道振动测量来进行状态或性能监测。本文展示了如何使用轨道挠度的累积分布函数来识别静止位置并从位移数据解释轨道移动的典型范围。此过程可用于校正速度或加速度数据中静止位置的偏移，确定向上和向下移动的比例，并将来自多个传感器的数据与公共数据对齐，以将偏转可视化为沿距离的函数。轨道。该技术提供了一种自动表征轨道位移的方法，可用作系统性能的衡量标准。这使得大量轨道振动数据可用于状态监测。