TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces a novel, integrated framework that leverages the power of transformer neural networks and deep reinforcement learning (DRL) algorithms to optimize maintenance actions. Our approach employs the transformer model to effectively capture complex temporal patterns in sensor data, thereby accurately predicting the Remaining Useful Life (RUL) of equipment. Simultaneously, the DRL component of our framework provides cost-effective and timely maintenance recommendations. We validate the efficacy of our framework on the NASA C-MPASS dataset, where it demonstrates significant advancements in both RUL prediction accuracy and the optimization of maintenance actions. Consequently, our pioneering approach provides an innovative data-driven methodology for prescriptive maintenance, addressing key challenges in industrial operations and leading the way to more efficient, cost-effective, and reliable systems.