Remaining Useful Life (RUL) prediction is a critical task that aims to estimate the amount of time until a system fails, where the latter is formed by three main components, that is, the application, communication network, and RUL logic. In this paper, we provide an end-to-end analysis of an entire RUL-based chain. Specifically, we consider a factory floor where Automated Guided Vehicles (AGVs) transport dangerous liquids whose fall may cause injuries to workers. Regarding the communication infrastructure, the AGVs are equipped with 5G User Equipments (UEs) that collect real-time data of their movements and send them to an application server. The RUL logic consists of a Deep Learning (DL)-based pipeline that assesses if there will be liquid falls by analyzing the collected data, and, eventually, sending commands to the AGVs to avoid such a danger. According to this scenario, we performed End-to-End 5G NR-compliant network simulations to study the Round-Trip Time (RTT) as a function of the overall system bandwidth, subcarrier spacing, and modulation order. Then, via real-world experiments, we collect data to train, test and compare 7 DL models and 1 baseline threshold-based algorithm in terms of cost and average advance. Finally, we assess whether or not the RTT provided by four different 5G NR network architectures is compatible with the average advance provided by the best-performing one-Dimensional Convolutional Neural Network (1D-CNN). Numerical results show under which conditions the DL-based approach for RUL estimation matches with the RTT performance provided by different 5G NR network architectures.

翻译：剩余寿命预测是一项关键任务，旨在估计系统发生故障前的剩余时间，而该故障由三个主要部分组成，即应用、通信网络和剩余寿命逻辑。本文对基于剩余寿命的完整链路进行了端到端分析。具体而言，我们考虑一个工厂车间，其中自动导引车运输危险液体，其掉落可能导致工人受伤。在通信基础设施方面，自动导引车配备5G用户设备，用于收集其运动的实时数据并将其发送至应用服务器。剩余寿命逻辑采用基于深度学习的流水线，通过分析收集的数据评估是否存在液体掉落风险，并最终向自动导引车发送指令以避免此类危险。基于此场景，我们进行了符合5G新无线电标准的端到端网络仿真，研究往返时间随系统总带宽、子载波间隔和调制阶数的变化关系。随后，通过真实世界实验收集数据，对7个深度学习模型和1个基线阈值算法进行训练、测试和比较，衡量其成本与平均提前量。最后，我们评估四种不同5G新无线电网络架构提供的往返时间是否与性能最优的一维卷积神经网络提供的平均提前量相匹配。数值结果表明，在何种条件下，基于深度学习的剩余寿命估计算法能够与不同5G新无线电网络架构提供的往返时间性能相适应。