基于马尔科夫链的投票率状态预测和寿命模拟.
Ziyi Wu1, Wanying He2, Ziyue Wu3
1School of Economics and Management, Beijing Jiaotong University, Beijing, China. 524717937@qq.com.
Scientific reports
|November 17, 2025
概括
本研究介绍了先进的图像处理和人工智能用于铁路点机状况评估,从而实现预测性维护. 优化的策略大大延长了设备的寿命,并提高了铁路安全.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 信号系统 信号系统
- 预测性维护是指预测性维护.
背景情况:
- 目前的铁路点点机器维护依赖于模糊的标准和硬的策略.
- 优化状态评估和维护对铁路安全至关重要.
研究的目的:
- 开发一个智能系统,用于点机状况评估和维护战略优化.
- 通过对关键信号设备进行预测性维护,提高铁路安全.
主要方法:
- 利用图像处理提取电流曲线进行条件评估.
- 开发了一个使用PCHIP插值和RMSE的曲线差异评估模型.
- 构建了一个具有最大概率估计和马尔科夫链用于状态预测的衰老模型.
主要成果:
- 根据开发的评估模型,将开关机器状态分为五个级别.
- 故障修复策略将物理寿命延长到14.73年,而维护策略为7.29年.
- 战略之间的经济寿命差异不到4年.
结论:
- 该研究为铁路信号设备的智能维护提供了一条途径.
- 这种方法有助于转向对关键基础设施的状态预测和精确维修.
- 改进的状况评估和预测性维护提高了铁路的整体运营效率和安全.
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