# Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

arXiv: https://arxiv.org/abs/2610.00363 · HTML: https://mlsanity.com/paper/2610.00363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 147 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Adapting Object Detectors with Conditional Domain Normalization](https://mlsanity.com/paper/2003.07071/) (2020)
- [A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models](https://mlsanity.com/paper/2410.07265/) (2024)
- [HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling](https://mlsanity.com/paper/2212.03354/) (2022)
- [Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices](https://mlsanity.com/paper/2309.06612/) (2023)
- [Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions](https://mlsanity.com/paper/2408.03747/) (2024)
- [A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN](https://mlsanity.com/paper/2311.14824/) (2023)
- [An Edge AI System Based on FPGA Platform for Railway Fault Detection](https://mlsanity.com/paper/2408.15245/) (2024)
- [Transformer Vibration Forecasting for Advancing Rail Safety and Maintenance 4.0](https://mlsanity.com/paper/2501.11730/) (2025)

## Abstract (arXiv)

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.