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This survey aims to evaluate the application of AI techniques in improving predictive maintenance for railway infrastructure.

February 2, 2026SensorsOpen Access

A Survey of AI-Enabled Predictive Maintenance for Railway Infrastructure: Models, Data Sources, and Research Challenges

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Survey reviews AI techniques improving predictive maintenance in railway infrastructure, suggesting future research opportunities.

This survey aims to evaluate the application of AI techniques in improving predictive maintenance for railway infrastructure.
Comprehensive literature review of AI applications in railway maintenance.
Comparison of various machine learning and deep learning models.
Analysis of relevant parameters like track geometry and vibration monitoring.
Evaluation of model performance and identification of research gaps.
Discussion of emerging technologies and methodologies in predictive maintenance.
Identified dominant AI models such as neural networks and random forests for maintenance tasks.
Highlighted issues with data quality and integration in existing models.
Outlined significant research gaps, especially in cross-network generalization and model robustness.
Emphasized opportunities for incorporating Digital Twins and edge AI for real-time management.

Bris-Peñalver et al. (2026) studied this question.

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