Leveraging Generative AI for Modelling and Optimization of Maintenance Policies in Industrial Systems

This paper explores how generative AI can enhance the modelling and optimization of maintenance policies by incorporating real-time problem-solving techniques into structured maintenance frameworks. Maintenance policies, evolving from simple calendar-dependent or age-dependent preventive maintenance strategies to more complex approaches involving partial system replacement, minimal repairs, or imperfect maintenance, have traditionally been optimized based on minimizing costs, maximizing reliability, and ensuring operational continuity. In this work, we leverage AI models to simulate and analyze the implementation and overlap of different maintenance strategies to an industrial asset, including the combined use of different preventive (total and partial replacement) and corrective actions (minimal repair and normal repairs), with perfect or imperfect maintenance results. Integrating generative AI with well-established maintenance policies and optimization criteria, this paper tries to demonstrate how AI-assisted tools can model maintenance scenarios dynamically, learning from predefined strategies and improving decision-making in real-time. Python-based simulations are employed to validate the approach, showcasing the benefits of using AI to enhance the flexibility and efficiency of maintenance policies. The results highlight the potential for AI to revolutionize maintenance optimization, particularly in single-unit systems, and lay the groundwork for future studies in multi-unit systems.