As urbanization progresses, metropolitan transit vehicles are encountering a growing frequency of curved pathways, which presents challenges pertaining to both the safety of the vehicles and the comfort of the passengers. There is no doubt that reliable acquisition of wheel-rail force is critical, since it has great significance for the safety and stability of vehicle operation. However, conventional wheel-rail force measurement methods are costly and difficult to use on in-service vehicles. A data-driven approach to inverting the wheel-rail force will overcome the above problems. In this work, a transfer learning-based residual long short-term memory neural network with temporal pattern attention mechanism (TPA-ResLSTM) is proposed to realize real-time monitoring of wheel-rail force, even in scenarios where the dataset is deficient in sufficient features. Initially, a learnable wheel-rail force inversion neural network model is developed based on the physical relationship that exists between the wheel-rail force and acceleration. Subsequently, a dynamic model for a B-type metro vehicle is utilized to simulate various scenarios, serving as a virtual source to provide data for the neural network. Afterward, the performance of the model is synthetically validated by the ablation study and field experimental data. Finally, the deep learning model is further improved by the transfer learning network, whose performance is comprehensively evaluated using limited data. The results show that the inversion model still has remarkable accuracy, in which the coefficient of determination is more than 0.9, under the case of limited training data. The proposed methodology diminishes the data requirements for the network while facilitating real-time monitoring and feedback regarding wheel-rail forces, thereby enhancing the realism of operational safety assessments for trains.

中文翻译：

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基于时间模式注意力机制的基于迁移学习的残差 LSTM 神经网络的轮轨力反转


随着城市化进程的推进，大都市公交车辆遇到越来越频繁的弯曲路径，这给车辆的安全性和乘客的舒适度带来了挑战。毫无疑问，可靠的轮轨力获取至关重要，因为它对车辆运行的安全性和稳定性具有重要意义。然而，传统的轮轨力测量方法成本高昂且难以在现役车辆上使用。一种数据驱动的倒置轮轨力的方法将克服上述问题。在这项工作中，提出了一种具有时间模式注意力机制的基于迁移学习的残差长短期记忆神经网络 （TPA-ResLSTM），即使在数据集缺乏足够特征的情况下，也能实现对轮轨力的实时监测。最初，基于轮轨力和加速度之间存在的物理关系开发了一个可学习的轮轨力反演神经网络模型。随后，利用 B 型地铁车辆的动态模型来模拟各种场景，作为虚拟源为神经网络提供数据。然后，通过消融研究和现场实验数据综合验证了模型的性能。最后，迁移学习网络进一步改进了深度学习模型，使用有限的数据对其性能进行了综合评估。结果表明，在训练数据有限的情况下，反演模型仍然具有显著的准确率，决定系数大于0.9。 所提出的方法减少了对网络的数据要求，同时促进了对轮轨力的实时监控和反馈，从而提高了列车运行安全评估的真实性。