  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.
