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

arXiv:2309.16935v2 Announce Type: replace
Abstract: 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 …

abstract algorithms arxiv capabilities cs.ai cs.lg demand downtime efficiency framework industrial maintenance networks neural networks paper predictive predictive maintenance reduce reinforcement reinforcement learning strategies systems transformer transformer model type