基于大数据的高速列车电磁测量数据分析

Xiaoying Sun, Guodong Wang
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摘要

列车的电磁环境与复杂多变的列车工况紧密相连,这使得对列车电磁环境的分析面临着参数过多、不能确定因果关系等挑战。基于列车电磁信号的测量理论,获得了BTM天线信号电缆和速度动态器的共模扰动频域信号,并对数据进行了预处理。然后,利用分类回归树(CART)模型和数据挖掘(DM)的径向基函数(RBF)神经网络模型对列车电磁测量数据进行分析,验证了模型的有效性。最后,对两种数据挖掘模型进行了比较。
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Analysis of high-speed train electromagnetic measurement data based on large data
The electromagnetic environment of the train and the complex and variable train conditions are closely linked, which makes the analysis of the electromagnetic environment for the train is facing too many parameters, can not determine the causal relationship and other challenges. Based on the measurement theory of train electromagnetic signal, this paper obtains the common mode disturbance frequency domain signal of the antenna signal cable of balise transmission module (BTM) and Speed dynal, and preprocesses the data. Then, the classification and regression tree (CART) model and the radial basis function (RBF) neural network model of data mining (DM) are used to analyze the electromagnetic measurement data of the train, and the validity of the model is verified. Finally, the two data mining models were compared.
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