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

TL;DR

This paper presents TranDRL, a novel framework combining Transformer neural networks and deep reinforcement learning to improve predictive maintenance by accurately forecasting equipment lifespan and optimizing maintenance actions.

Contribution

It introduces an integrated Transformer-DRL framework that enhances RUL prediction accuracy and maintenance decision-making in industrial systems.

Findings

Significant improvement in RUL prediction accuracy on NASA C-MPASS dataset.

Effective optimization of maintenance schedules reducing downtime.

Demonstrates superiority over existing machine learning methods.

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 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.