Locomotive overhaul 4.0: a conceptual framework for predictive and adaptive maintenance

Purpose This study investigates the integration of Industry 4.0 technologies into the locomotive overhaul process, aiming to enhance efficiency, reduce variability and improve quality. The research explores the potential of Augmented Reality, IoT, Digital Twins and AI, in optimizing maintenance procedures by providing real-time data-driven insights, predictive diagnostics and interactive instructions for operators. The project also examines how these technologies contribute to continuous improvement and sustainability. Method The research follows a structured approach, beginning with a Google Scholar literature review, focusing on Industry 4.0 applications in maintenance, locomotive overhaul and process optimization. Ten scientific articles were selected to form the basis of the Locomotive Overhaul 4.0 framework, integrating real-time data collection, AI-driven analysis and predictive modeling. The methodology was structured into three phases: (i) identification of key technologies and their feasibility in locomotive maintenance; (ii) theoretical comparison with selected use cases from adjacent sectors; (iii) conceptual simulation of workflow enhancements, grounded in established principles of Digital Twins and AI integration. Findings IoT sensors enable continuous monitoring of locomotive components, feeding real-time data into Digital Twins to simulate operational conditions and predict failures. AI-based diagnostics analyzes potential faults, reducing unplanned downtime and improving accuracy. Augmented Reality interfaces provide technicians with real-time guidance, leading to process standardization and reduced human errors. These technologies support a data-driven continuous improvement cycle, enhancing service efficiency and environmental sustainability by minimizing unnecessary recalls and resource waste. Conclusions Industry 4.0 technologies significantly enhance the precision, efficiency and sustainability of locomotive overhaul processes. The study suggests that further research should focus on AI-driven automation, adaptive AR interfaces and integrated predictive maintenance models to maximize the benefits of digital transformation in railway maintenance.