Smart Railways: Predictive Maintenance, Digital Twins, and Energy-Aware Operations
Keywords:
Smart railways, predictive maintenance, digital twin; energy-aware operations, sustainability, artificial intelligence, IoT, railway digitalizationAbstract
This review aims to synthesize and conceptualize the convergence of predictive maintenance, digital twin ecosystems, and energy-aware operations in the transformation of smart railway systems toward reliability, adaptability, and sustainability. This study employed a qualitative systematic review design, focusing exclusively on secondary data derived from peer-reviewed literature. A total of 13 articles published between 2015 and 2025 were selected from major databases, including Scopus, IEEE Xplore, and ScienceDirect, using targeted keywords related to smart railways, predictive maintenance, digital twins, and energy optimization. Data collection followed a rigorous screening process based on relevance, methodological quality, and conceptual contribution, continuing until theoretical saturation was achieved. The data were analyzed through thematic coding using Nvivo 14 software, applying open, axial, and selective coding to identify emergent patterns and interrelationships across the studies. The resulting framework captured the interdependencies between technological enablers, operational intelligence, and sustainable outcomes in smart railway systems. Three major themes emerged from the analysis: (1) Predictive maintenance and asset intelligence, emphasizing IoT-enabled sensing, machine learning diagnostics, and human–AI collaboration for condition-based maintenance optimization; (2) Digital twin ecosystems and cyber-physical synchronization, highlighting lifecycle integration, real-time simulation, and security governance as key enablers of system adaptability; and (3) Energy-aware operations and sustainable rail systems, demonstrating the role of algorithmic energy optimization, smart grids, and renewable integration in reducing carbon intensity and enhancing energy efficiency. The findings revealed that the interaction between these domains forms a cohesive digital ecosystem enabling real-time, self-learning, and resource-efficient railway operations. The integration of predictive maintenance, digital twin technology, and energy-aware strategies establishes a transformative model for future railways—one that balances operational reliability, environmental sustainability, and intelligent automation. However, challenges remain in standardization, data governance, and human–AI trust, necessitating further interdisciplinary research and policy development.
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