基于卷积神经网络的铁路波纹预测,使用车辆的加速测量
Masoud Haghbin1, Juan Chiachío1, Sergio Muñoz2
1Department of Structural Mechanics and Hydraulic Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), 18001 Granada, Spain.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究使用深度学习 (CNN-1D) 来从火车运动数据中预测铁路波纹. 该模型准确预测波纹形状,使铁路能够进行主动维护.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 机器学习 机器学习
- 预测性维护是指预测性维护.
背景情况:
- 铁路波纹是影响铁路基础设施和运营的一个重大问题.
- 准确预测铁路波纹对于高效的维护策略至关重要.
研究的目的:
- 开发和验证用于预测铁路波纹的深度学习模型.
- 使用车载传感器数据 (垂直加速和前进速度) 评估模型的性能.
主要方法:
- 一维卷积神经网络 (CNN-1D) 的实施.
- 使用车载轨道车辆的垂直加速和前进速度测量.
- 使用梯度加权类激活映射 (Grad-CAM) 来实现模型的可解释性.
主要成果:
- 在CNN-1D模型中,在预测波纹形状时,平均绝对百分比误差低于5%.
- 该模型展示了基于实时传感器数据复制波纹的能力.
- 格拉德-CAM分析证实了该模型能够识别不同的波纹区域.
结论:
- 像CNN-1D这样的数据驱动方法显示了对实时铁路波纹预测的巨大潜力.
- 这种预测能力可以提高铁路维护的可靠性和效率.
- 在线监测轨道车辆动态,为主动基础设施管理提供了可行的途径.
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