AI-Powered Predictive Maintenance in Hybrid and Electric Rail Systems

  • Authors

    • Aakansha Soy Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    • Dr. Priya Vij Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    https://doi.org/10.14419/rn9hbx42

    Received date: May 2, 2025

    Accepted date: May 26, 2025

    Published date: July 8, 2025

  • Environment; Electric Rail Systems; AI; Machine Learning; Fault Detection; Hybrid

  • Abstract

    Strategic transportation modifications in rail infrastructure brought both environmental benefits and operational improvements through ‎hybrid and electric rail systems. Tomorrow’s rail networks need reliable maintenance, so new ways must be found to keep it safe and ‎affordable. Scientists are researching the deployment of artificial intelligence (AI) systems to do predictive maintenance for electric and ‎hybrid electric rail networks. With predictive maintenance based on AI, operators are now using real-time monitoring along with data analytics and ‎machine learning algorithms to detect equipment failures before they happen. By doing this, trains can run longer without requiring long ‎periods of rest. AI tools are evaluating sensor data from engine units, batteries, and traction devices to detect future equipment failures. ‎Predictive maintenance is possible with this system. A study looks at AI methods that involve fault detection analytics and machine learning ‎and shows their applications in the rail industry. AI-powered hybrid electric rail applications will demonstrate their real-life implementations ‎during the presentation and show operational improvements and safety enhancements, and cost savings. This paper reviews data integration ‎challenges and looks at regulatory and system complexity requirements to provide research recommendations for future infrastructure ‎development. The rail industry wants an operational evolution enabled by predictive maintenance systems driven by AI to build better rail ‎systems with less environmental impact‎.

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  • How to Cite

    Soy, A. ., & Vij , D. P. . (2025). AI-Powered Predictive Maintenance in Hybrid and Electric Rail Systems. International Journal of Basic and Applied Sciences, 14(SI-1), 13-17. https://doi.org/10.14419/rn9hbx42