影响因子：5.9
DOI码：10.1109/TIM.2025.3565351
所属单位：西南交通大学
发表刊物：IEEE Transactions on Instrumentation and Measurement
刊物所在地：美国
关键字：Conditional generative adversarial network (CGAN),high-speed railway,pantograph–catenary system (PCS),surrogate model
摘要：In electric railways, the current collection quality of pantograph–catenary systems (PCSs) is typically evaluated through numerical simulations using the finite element method, which is computationally expensive and time-consuming. To address this challenge, we propose a surrogate modeling approach that trains a conditional generative model to approximate the output of the reference numerical model. Specifically, we introduce dual-domain conditional generative adversarial networks (DD-CGAN) to generate contact force (CF) curves for various PCS parameter configurations. The generator network takes system parameters as input and produces the corresponding CF curve, while the discriminator network distinguishes between real and predicted curves in both the time and frequency domains, ensuring greater consistency. Furthermore, the feature fusion module is proposed to extract and integrate timeand frequency-domain features by using a multiscale channel attention (MSCA) mechanism. Extensive experimental results demonstrate the effectiveness and advantages of DD-CGAN for surrogate modeling of pantograph–catenary interactions. The CF curves generated by our method exhibit high consistency with simulation results from high-fidelity numerical models with a mean absolute error (MAE) of 0.9815, which is six times more accurate than state-of-the-art methods. Most importantly, our method achieves a speedup of nearly 1000× compared to traditional numerical simulations, highlighting its potential for practical use in designing and optimizing catenary structural parameters.
合写作者：Hui Wang, Xudan Wang, Xiangyu Meng, Yang Song, Zhigang Liu
第一作者：Jialin Guan
论文类型：SCI
通讯作者：Jialin Guan
论文编号：2528313
学科门类：工学
一级学科：仪器科学与技术
卷号：74
页面范围：72
ISSN号：1557-9662
是否译文：是
发表时间：2025-04-01