一种基于机器学习的方法用于铁路轨道监控,使用在服务中的火车上测量的加速度
Abdollah Malekjafarian1, Chalres-Antoine Sarrabezolles2, Muhammad Arslan Khan1
1Structural Dynamics and Assessment Laboratory, School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一种新的铁路轨道监测方法,使用火车加速数据来检测度损失. 人工神经网络 (ANN) 通过分析加速信号能量,有效地识别轨道损伤.
科学领域:
- 土木工程 土木工程是指土木工程.
- 铁路工程 铁路工程是指铁路工程.
- 结构健康监测 结构健康监测
背景情况:
- 铁路轨道的退化,特别是子层的刚性丧失,可能会损害安全和运营效率.
- 传统的轨道检查方法往往是劳动密集型,昂贵,并且可能无法有效地检测地下问题.
- 开发非破坏性监测技术对于积极维护和确保铁路基础设施完整性至关重要.
研究的目的:
- 提出并验证一种新的方法,用于检测铁路轨道子层的刚性损失,使用在使用中的列车测量.
- 开发一种人工神经网络 (ANN) 模型,能够根据加速反应能量识别轨道损伤.
- 引入一个损坏指标 (DI) 用于可视化和量化轨道退化.
主要方法:
- 从正在运行的列车中利用的加速度响应,处理为每15米轨道切片的能量值.
- 开发和训练了一种ANN模型,使用模拟和真实世界的火车加速度数据来实现健康的轨道条件.
- 模拟轨道损坏,通过降低下压层的刚性,代表悬挂的卧床.
- 引入了基于模拟和预测能量之间的预测错误的DI,以评估损坏水平.
- 对DI的性能进行了灵敏度分析,考虑了信号噪声,切片大小和多个损伤位置.
主要成果:
- 该ANN模型成功地从加速信号能量中学习了健康的轨道条件.
- 拟议的损坏指标 (DI) 有效地可视化和区分了轨道子层中各种级别的刚性损失.
- 灵敏度分析表明DI对中等信号噪声和切片大小变化的稳定性.
- 该方法在识别多个受损位置方面显示出潜力,尽管性能各不相同.
结论:
- 开发的基于ANN的方法为监测铁路轨道下层刚度提供了一个有希望的,非破坏性的方法.
- 拟议的损坏指标为可视化和评估轨道退化提供了有价值的工具,有助于主动维护.
- 建议进行进一步的研究,以完善DI的复杂场景,包括不同的火车速度和各种损坏类型.
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