An End-To-End Analysis of Deep Learning-Based Remaining Useful Life Algorithms for Satefy-Critical 5G-Enabled IIoT Networks

TL;DR

This paper analyzes the end-to-end process of using deep learning for RUL prediction in safety-critical 5G-enabled IIoT networks, combining network simulations, real-world data, and model comparisons.

Contribution

It provides a comprehensive analysis of RUL prediction in a 5G IIoT context, including network performance, model evaluation, and compatibility assessment.

Findings

DL models outperform threshold-based algorithms in RUL prediction

RTT performance varies with network parameters and affects RUL estimation

Best 1D-CNN model's prediction aligns with 5G network RTT under certain conditions

Abstract

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.