基于多保真度TDNN与物理信息残差学习的铁路转向架响应预测可靠性研究
Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning
- Hanyang University（汉阳大学）
- Korea Railroad Research Institute（韩国铁道技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对铁路转向架响应预测，提出结合多保真度TDNN与物理信息残差学习的修正方法，利用仿真与试验数据，在385 km/h工况下实现高精度预测。
AI中文摘要：
铁路工程师需要能够预测车辆在各种无法穷尽测试的运行场景下响应的仿真模型。与代表性测量结果的一致性提供了关键证据，但在有限条件下的校准并不能保证在其他条件下的准确性。我们提出了一种多保真度铁路转向架响应修正方法，将多体仿真历史视为低保真度信息，将滚轮试验台测量视为高保真度证据。该方法将实验锚定的保真度分配与物理信息差异学习相结合，用于多通道转向架响应历史。时间延迟神经网络（TDNN）表示条件相关的仿真趋势，开发拟合的幅值对齐定义低保真度基线。随后，残差修正网络对基线未能解释的可复现响应分量进行建模，并将其添加到基线中。有效的动态平衡方程通过表示仿真系统与物理系统之间在惯性、阻尼、刚度和外部激励方面的差异来约束学习到的差异。训练目标将该约束与残差匹配、时间平滑性以及选择性应用的位移-加速度一致性项相结合。在评估的重构案例中，修正后的响应给出了平均决定系数0.8197，平均归一化均方根误差（NRMSE）为4.6055%，平均归一化平均绝对误差（NMAE）为1.9297%。这些结果为在留出的385 km/h工况下准确预测响应提供了初步证据。
英文摘要：
Railway engineers need simulation models that predict vehicle responses across operating scenarios that cannot be tested exhaustively. Agreement with representative measurements provides essential evidence, but calibration at a limited set of conditions does not guarantee accuracy elsewhere. We present a multifidelity railway-bogie response-correction method that treats multibody simulation histories as low-fidelity information and roller-rig measurements as high-fidelity evidence. This method combines an experiment-anchored fidelity assignment with physics-informed discrepancy learning for multichannel bogie-response histories. A time-delay neural network (TDNN) represents the condition-dependent simulation trend, and development-fitted amplitude alignment defines the low-fidelity baseline. A residual-correction network then models the reproducible response component not explained by this baseline and adds it to the baseline. An effective dynamic-balance equation constrains the learned discrepancy by representing differences in inertia, damping, stiffness, and external forcing between the simulated and physical systems. The training objective combines this constraint with residual matching, temporal smoothness, and a combined channel-2 acceleration loss selected using displacement-acceleration consistency evidence. For the evaluated reconstruction case, the corrected response gives a mean coefficient of determination of 0.8197, a mean normalized root-mean-square error (NRMSE) of 4.6055 %, and a mean normalized mean absolute error (NMAE) of 1.9297 %. These results provide initial evidence of accurate response prediction at the held-out 385 km/h condition.
