Abstract This paper presents a method for the experimental measurement of the lateral wheel-rail contact force based on Artificial Neural Networks (ANN). It is intended to demonstrate how an Artificial Intelligence (AI) method proves to be a valid alternative to other approaches based on sophisticated mathematical models when it is applied to the wheel-rail contact force measurement problem. This manuscript addresses the problem from a computational and experimental approach. The artificial intelligence algorithm has been experimentally tested in a real scenario using a 1:10 instrumented scaled railway vehicle equipped with a dynamometric wheelset running on a 5-inch-wide track. The obtained results show that the ANN approach is an easy and computationally efficient method to measure the applied lateral force on the instrumented wheel that requires the use of fewer sensors.

中文翻译：

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人工神经网络在轮轨横向接触力测量中的应用：与谐波消除法的比较

摘要 本文提出了一种基于人工神经网络（ANN）的轮轨横向接触力实验测量方法。它旨在展示人工智能 (AI) 方法在应用于轮轨接触力测量问题时如何证明是基于复杂数学模型的其他方法的有效替代方法。这份手稿从计算和实验方法解决了这个问题。该人工智能算法已在真实场景中使用 1:10 仪表化比例铁路车辆进行了实验测试，该铁路车辆配备了在 5 英寸宽的轨道上运行的测力轮对。