TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework
This provides an incremental data-driven framework for industrial systems to reduce downtime and costs through improved maintenance scheduling.
The paper tackles predictive maintenance by integrating Transformer models for remaining useful life prediction with deep reinforcement learning for maintenance optimization, achieving significant improvements in accuracy and action optimization on the NASA C-MPASS dataset compared to existing methods.
Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system 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 an equipment. Additionally, 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, compared to the other prevalent machine learning-based methods. Our proposed approach provides an innovative data-driven framework for industry machine systems, accurately forecasting equipment lifespans and optimizing maintenance schedules, thereby reducing downtime and cutting costs.