Proactive Fault Tolerance for 5G Smart Factories: Transformer-Based Deep Reinforcement Learning for Reliability-Oriented Redundancy Allocation

The widespread adoption of 5G technology has significantly enhanced the interconnectivity and automation of factory equipment, increasing the complexity of device coordination. Equipment failures or performance degradation can result not only in individual device downtime but also in cascading disruptions across the entire production line, potentially leading to factory-wide shutdowns and decreased production efficiency. Ensuring high equipment reliability—particularly fault tolerance under extreme conditions—is therefore critical to the stable operation of a 5G fully connected factory. To address this challenge, this paper proposes a proactive fault tolerance (FT) framework that enhances system reliability through parallel redundancy strategies. The framework begins by evaluating system reliability based on device responses in the most recent service cycle. If the computed FT value falls below the threshold specified by the Service Level Agreement (SLA), the framework is triggered. Devices are then ranked according to their historical performance and their criticality in the upcoming production cycle. A “1+N” redundancy allocation strategy is applied, and the resulting Integer Nonlinear Programming (INLP) problem is solved using a Redundancy Allocation Algorithm based on Transformer-enhanced Deep Reinforcement Learning (RAA-TDRL). To validate the proposed framework, we conduct simulations using a real-world dataset to emulate performance fluctuations. Experimental results show that the proposed key equipment identification method and RAAT-DRL algorithm achieve superior performance and reduced time complexity compared to baseline approaches.