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Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System.

Mahyar Jafar Kazemi1, Maria Rashidi1, Won-Hee Kang1

  • 1Centre for Infrastructure Engineering, Western Sydney University, Sydney, NSW 2751, Australia.

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Summary

Digital Twin (DT) technology offers promise for railway maintenance, but applications are fragmented. This review highlights gaps in standardization, scalability, and real-world validation for effective DT-driven railway upkeep.

Keywords:
Artificial intelligence (AI)Condition-Based Maintenance (CBM)Digital Twin (DT) systemsInternet of Things (IoT)cyber-physical systemspredictive and optimised maintenancesmart railway infrastructure

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Area of Science:

  • Railway Engineering
  • Cyber-Physical Systems
  • Predictive Maintenance

Background:

  • Digital Twin (DT) technology is emerging for optimizing railway maintenance.
  • Current DT applications in railways are fragmented and lack systematic evaluation.
  • A DT integrates physical assets, virtual models, and data exchange for real-time insights.

Purpose of the Study:

  • To critically review DT-enabled systems for monitoring, analysis, and maintenance decision-support in railway engineering.
  • To identify research gaps and future directions for DT applications in railways.
  • To synthesize current DT implementations and highlight challenges for scalable frameworks.

Main Methods:

  • Systematic review of 34 peer-reviewed studies (2020-2025) on DT in railway infrastructure and operations.
  • In-depth analysis of 10 key studies focusing on technical implementation and data integration.
  • Categorization of applications across track systems, rolling stock, bridges, and communication networks.

Main Results:

  • DT approaches enhance fault detection and enable condition-based/predictive maintenance.
  • DTs can reduce the need for manual inspections in railway systems.
  • Most current studies are conceptual or pilot-scale with limited real-world validation.

Conclusions:

  • Significant challenges hinder scalable DT implementation in railways, including standardization, interoperability, and real-time scalability.
  • Data governance, cybersecurity, and integration of multi-source sensing/analytics require further attention.
  • Addressing these gaps is crucial for reliable, integrated DT-driven railway maintenance frameworks.