Kuźnar Małgorzata, Lorenc Augustyn
Department of Rail Vehicles and Transport, Cracow University of Technology, 31-878 Cracow, Poland.
Materials (Basel). 2021 Dec 23;15(1):98. doi: 10.3390/ma15010098.
The impact of the pantograph of a rail vehicle on the overhead contact line depends on many factors. Among other things, the type of pantograph, i.e., the material of the sliding strip, influences the wear and possible damage to the sliding strip. The possibility of predicting pantograph failures may make it possible to reduce the number of these kinds of failures. This article presents a method for predicting the technical state of the pantograph by using artificial neural networks. The presented method enables the prediction of the wear and damage of the pantograph, with particular emphasis on carbon sliding strips. The paper compares 12 predictive models based on regression algorithms, where different training algorithms and activation functions were used. Two different types of training data were also used. Such a distinction made it possible to determine the optimal structure of the input and output data teaching the neural network, as well as the determination of the best structure and parameters of the model enabling the prediction of the technical condition of the current collector.
轨道车辆受电弓对架空接触线的影响取决于许多因素。其中，受电弓的类型，即滑板的材料，会影响滑板的磨损及可能出现的损坏。预测受电弓故障的可能性或许能够减少此类故障的数量。本文提出了一种利用人工神经网络预测受电弓技术状态的方法。所提出的方法能够预测受电弓的磨损和损坏，尤其侧重于碳滑板。本文比较了基于回归算法的12种预测模型，这些模型使用了不同的训练算法和激活函数。还使用了两种不同类型的训练数据。这种区分使得确定用于训练神经网络的输入和输出数据的最佳结构成为可能，同时也能够确定能够预测集电器技术状态的模型的最佳结构和参数。
