Maintenance Decision

Maintenance decision-making is evolving from reactive, human-driven processes to proactive, data-informed strategies aimed at optimizing resource allocation and minimizing downtime. Current research emphasizes leveraging data-driven models, including transfer learning and deep reinforcement learning, to predict equipment failures, prioritize maintenance tasks based on multiple criteria (e.g., risk, cost), and optimize maintenance schedules. This shift towards predictive and prescriptive maintenance has significant implications for improving efficiency, safety, and sustainability across various sectors, from transportation infrastructure to industrial cyber-physical systems.