Data underlying the publication: WaveletInception Networks for Drive-by Vibration-Based Infrastructure Health Monitoring

The dataset is an Axle-Box Acceleration simulation dataset based on the vehicle-track interaction model developed by Dr. Chen Shen, as described in the following reference. The rail and sleepers are meshed using Timoshenko beam elements, while the ballast and railpads are represented as discrete spring-damper pairs.Clamps and bolts are not explicitly modeled; instead, their stiffness is incorporated into the railpad stiffness, a widely accepted simplification in railway track modeling. The wheel is simplified as a rigid mass, and the wheel-rail contact is modeled using a Hertzian spring. <br>ABA measurements are simulated at four operational speeds of 40, 50, 55, and 65 km/h, considering floating stiffness reduction. This is the raw dataset, and additive white Gaussian noise scenarios are generated as the paper explains. For details on the dataset, please refer to the cited papers.<br>If you use this dataset, ensure proper citation of the following references.<br>*Shen, C., P. Zhang, R. Dollevoet, A. Zoeteman, and Z. Li, Evaluating Railway Track Stiffness Using Axle Box Accelerations: A Digital Twin Approach. Mechanical Systems and Signal Processing, vol.21 204, 2023, p. 110730.22.<br>*Chen Shen, Rolf Dollevoet, Zili Li, Fast and robust identification of railway track stiffness from simple field measurement, Mechanical Systems and Signal Processing,Volume 152, 2021, 107431.<br>*R. R. Samani, A. Nunez, and B. De Schutter. A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration response. Dec. 3, 2024. doi: 10 . 48550 / arXiv.2412.02643. arXiv: 2412.02643 [cs]. Pre-published.

本数据集为基于陈申博士开发的车轨相互作用模型构建的轴箱加速度（Axle-Box Acceleration，ABA）仿真数据集，相关细节见下述参考文献。
钢轨与轨枕采用铁木辛柯梁单元（Timoshenko beam elements）进行网格划分，道砟与轨下胶垫被建模为离散弹簧-阻尼单元对。扣件与螺栓未显式建模，而是将其刚度并入轨下胶垫刚度中——这是铁路轨道建模领域广泛认可的简化方案。车轮被简化为刚性质量块，轮轨接触采用赫兹弹簧（Hertzian spring）模型进行描述。
本仿真在40、50、55与65 km/h四种运行速度下生成轴箱加速度数据，并考虑了浮置刚度折减效应。本数据集为原始数据集，按照论文所述方法生成了附加高斯白噪声场景。如需了解该数据集的详细信息，请参阅所引用的文献。
若您使用本数据集，请务必正确引用下述参考文献：
* Shen, C., Zhang, P., Dollevoet, R., Zoeteman, A. & Li, Z. 《Evaluating Railway Track Stiffness Using Axle Box Accelerations: A Digital Twin Approach》，《机械系统与信号处理》（Mechanical Systems and Signal Processing），第21卷第204期，2023年，第110730.22页。
* Shen, C., Dollevoet, R. & Li, Z. 《Fast and robust identification of railway track stiffness from simple field measurement》，《机械系统与信号处理》（Mechanical Systems and Signal Processing），第152卷，2021年，第107431页。
* Samani, R. R., Nunez, A. & De Schutter, B. 《A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration response》，2024年12月3日，DOI: 10.48550/arXiv.2412.02643，arXiv:2412.02643 [cs]，预印本。