arXiv：cs.LG（机器学习，全量分类）· Ammar Bouketta, Smail Niar, Hamza Ouarnoughi·· 14 小时前AI 评分25
铁路系统异常检测的深度学习：一篇结构化综述
Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey
AI 导读
一篇发表于 EAAI 的综述系统梳理了铁路系统深度学习异常检测方法，按异常位置、数据表示、传感模态和时间特征建立统一分类体系。方法被归为分类式、预测式、重建式与混合四类范式，涵盖卷积、循环、注意力架构、自编码器、GAN 与 Transformer。文章还讨论边缘-云部署、算力约束与硬件感知优化，并给出连接异常特征、数据属性与运营约束的决策框架。
正文
Abstract: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.
| Comments: | Survey paper. Published in Engineering Applications of Artificial Intelligence (EAAI), 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.00363 [cs.LG] |
| (or arXiv:2610.00363v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00363 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Engineering Applications of Artificial Intelligence, Volume 181, Part 7, Article 115776, 2026 |
| Related DOI: | https://doi.org/10.1016/j.engappai.2026.115776
DOI(s) linking to related resources |
Submission history
From: Ammar Bouketta [view email]
[v1]
Wed, 30 Sep 2026 06:29:59 UTC (7,933 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org