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：剩余使用寿命（RUL）预测是一项关键任务，旨在估计系统故障前的时间长度，该系统由应用层、通信网络和RUL逻辑三个主要部分组成。本文对整个基于RUL的链条进行了端到端分析。具体而言，我们考虑了一个工厂车间场景，其中自动导引运输车（AGVs）运输危险液体，液体泄漏可能导致工人受伤。在通信基础设施方面，AGVs配备了5G用户设备（UEs），用于收集其运动的实时数据并发送至应用服务器。RUL逻辑采用基于深度学习（DL）的流水线，通过分析收集的数据评估液体是否可能泄漏，并最终向AGVs发送指令以避免此类危险。基于此场景，我们执行了符合5G新空口（5G NR）标准的端到端网络仿真，以研究往返时间（RTT）作为系统总带宽、子载波间隔和调制阶数的函数。随后，通过真实世界实验收集数据，用于训练、测试和比较7个深度学习模型及1个基于基线阈值的算法，评估指标包括成本和平均提前时间。最后，我们评估了四种不同5G NR网络架构提供的RTT是否与表现最优的一维卷积神经网络（1D-CNN）所提供的平均提前时间相匹配。数值结果表明，在何种条件下，基于深度学习的RUL估计方法能够与不同5G NR网络架构提供的RTT性能相匹配。