发表机构
Université Polytechnique Hauts-de-France; Alstom; INSA Hauts-de-France; University of Sharjah（上法兰西理工大学; 阿尔斯通; 法国国立应用科学学院上法兰西分校; 沙迦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文对铁路系统异常检测的深度学习方法进行结构化综述，提出统一分类法，并构建面向决策的框架以指导检测范式和部署配置的选择。
AI 中文摘要
确保现代铁路系统安全可靠运行越来越依赖于数据驱动的监测和智能故障检测。深度学习已成为铁路异常检测的有效范式，这得益于来自机车车辆和基础设施的异构传感器数据日益丰富。本文对基于深度学习的铁路系统异常检测方法进行了结构化综述。所综述的方法使用统一的分类法进行组织，涵盖异常位置、数据表示与表现形式、传感模态和时间特征。现有方法，包括卷积、循环和基于注意力的架构、自编码器、生成对抗网络和变换器，被结构化为基于分类、基于预测、基于重建和混合学习范式。该综述还审视了以数据为中心的挑战、评估实践、性能指标和实际部署方面，包括边缘-云架构、计算约束和硬件感知优化。最后，一个面向决策的框架将异常特征、数据属性和操作约束与合适的检测范式和部署配置联系起来。这项工作为选择和部署用于铁路异常检测的深度学习解决方案提供了结构化参考，并强调了迈向可靠和可扩展的智能监测系统的开放挑战。
英文摘要
Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effective paradigm for railway anomaly detection, driven by the growing availability of heterogeneous sensor data from rolling stock and infrastructure. This paper presents a structured survey of deep learning-based anomaly detection approaches for railway systems. The surveyed methods are organized using a unified taxonomy covering anomaly location, data representation and manifestation, sensing modality, and temporal characteristics. Existing approaches, including convolutional, recurrent and attention-based architectures, autoencoders, generative adversarial networks, and transformers, are structured into classification-based, prediction-based, reconstruction-based, and hybrid learning paradigms. The survey also examines data-centric challenges, evaluation practices, performance metrics, and practical deployment aspects, including edge-cloud architectures, computational constraints, and hardware-aware optimization. Finally, a decision-oriented framework links anomaly characteristics, data properties, and operational constraints to suitable detection paradigms and deployment configurations. This work provides a structured reference for selecting and deploying deep learning solutions for railway anomaly detection and highlights open challenges toward reliable and scalable intelligent monitoring systems.
CommentsSurvey paper. Published in Engineering Applications of Artificial Intelligence (EAAI), 2026
Journal refEngineering Applications of Artificial Intelligence, Volume 181, Part 7, Article 115776, 2026
DOI：10.1016/j.engappai.2026.115776
