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A data fusion approach for track monitoring from multiple in-service trains

机译:一种数据融合方法,用于从多个在役列车中进行轨道监控

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We present a data fusion approach for enabling data-driven rail-infrastructure monitoring from multiple in-service trains. A number of researchers have proposed using vibration data collected from in-service trains as a low-cost method to monitor track geometry. The majority of this work has focused on developing novel features to extract information about the tracks from data produced by individual sensors on individual trains. We extend this work by presenting a technique to combine extracted features from multiple passes over the tracks from multiple sensors aboard multiple vehicles. There are a number of chal-lenges in combining multiple data sources, like different relative position coordinates depending on the location of the sensor within the train. Furthermore, as the number of sensors increases, the likelihood that some will malfunction also increases. We use a two-step approach that first minimizes position offset errors through data alignment, then fuses the data with a novel adaptive Kalman filter that weights data according to its esti-mated reliability. We show the efficacy of this approach both through simulations and on a data-set collected from two instrumented trains operating over a one-year period. Combining data from numerous in-service trains allows for more continuous and more reliable data-driven monitoring than analyzing data from any one train alone; as the num-ber of instrumented trains increases, the proposed fusion approach could facilitate track monitoring of entire rail-networks.
机译:我们提出了一种数据融合方法,用于从多个在役列车中实现数据驱动的铁路基础设施监控。许多研究人员已提出使用从在役火车上收集的振动数据作为监视轨道几何形状的低成本方法。这项工作的大部分致力于开发新颖的功能,以从由各个列车上的各个传感器产生的数据中提取有关轨道的信息。我们通过提出一种技术来组合这项工作,该技术可以将来自多个车辆上多个传感器的轨迹上多次通过的提取特征进行组合。组合多个数据源存在很多挑战,例如取决于列车内传感器的位置的不同相对位置坐标。此外,随着传感器数量的增加,一些传感器可能发生故障的可能性也随之增加。我们采用两步法,首先通过数据对齐将位置偏移误差降至最低,然后将数据与新型自适应卡尔曼滤波器融合,然后根据估计的可靠性对数据进行加权。我们通过模拟以及在一年中运营的两辆仪表火车上收集到的数据集上展示了这种方法的有效性。与单独分析任何一列火车的数据相比,将众多在役火车的数据进行组合可以实现更连续,更可靠的数据驱动的监控;随着仪表火车数量的增加,提出的融合方法可以促进整个铁路网络的轨道监控。

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