对铁路基础设施的人工智能预测维护的调查:模型,数据源和研究挑战
Francisco Javier Bris-Peñalver1,2, Randy Verdecia-Peña2,3, José I Alonso2,4
1Directorate of Maintenance and Conservation, Administrador de Infraestructuras Ferroviarias (ADIF), 28020 Madrid, Spain.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
人工智能 (AI) 通过基于条件的策略来增强铁路维护. 本次调查审查了用于铁路基础设施的AI技术,以提高智能维护生态系统的安全性和效率.
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
- 可持续的移动性 可持续的移动性
背景情况:
- 数字化加速了铁路运输中的基于条件的维护 (CBM).
- 当前维护通常是反应性的,限制了安全性和优化.
- 人工智能为维护铁路基础设施提供先进的解决方案.
研究的目的:
- 对铁路维护人工智能技术进行全面审查.
- 在这个领域分析机器学习和深度学习应用.
- 确定智能铁路维护的挑战和机遇.
主要方法:
- 在预防性,预测性和规范性维护中对人工智能应用进行系统审查.
- 分析机器学习和深度学习模型 (例如神经网络,SVM,随机森林).
- 应用AI用于跟踪几何,振动和成像数据的评估.
主要成果:
- 人工智能技术在提高铁路维护方面表现有前途.
- 确定了主要的数据源和特征工程方法.
- 研究差距包括数据质量,概括性,稳定性和整合性.
结论:
- 人工智能对于自主铁路基础设施管理至关重要.
- 像数字双胞胎和边缘AI这样的新兴领域是关键.
- 这项调查指导了智能铁路维护的未来研究和标准化.
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