基于改进错误的PSO-BiLSTM的高速铁路接触系统的剩余有用寿命预测和早期预警模型
Li Liu1, Yuting Gan2, ZiJian Deng1
1School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, 330013, China.
Scientific reports
|January 6, 2026
概括
本研究引入了用于高速铁路接触系统的先进预警系统. 该模型准确地预测了剩余的使用寿命 (RUL) 和潜在的故障,从而实现主动维护和提高安全性.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 人工智能的人工智能
- 预测性维护是指预测性维护.
背景情况:
- 高铁连接系统的稳定性对于火车的安全性和运营效率至关重要.
- 关于剩余使用寿命 (RUL) 的不清楚预测导致维护和关闭造成的重大经济损失.
研究的目的:
- 开发一个早期预警模型来预测高速铁路接触系统的RUL.
- 通过及时维护和防止意外故障,减少经济损失.
主要方法:
- 为RUL预测,开发了一种改进错误的双向长期和短期内存网络 (BiLSTM) 模型.
- 极端梯度提升 (XGBoost) 用于纠正预测错误,核密度估计 (KDE) 确定了警告值.
主要成果:
- 拟议的PSO-BiLSTM模型实现了根平均平方误差 (RMSE) 的11.6293和平均绝对误差 (MAE) 的7.9643.
- 早期预警系统成功地预测了接触系统的故障,提前30天.
结论:
- 开发的模型为高速铁路接触系统提供了准确的RUL预测和早期故障警告.
- 该系统有助于及时维护,识别潜在风险,并提高整体运营安全性和可靠性.
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