System Reliability Engineering in the Age of Industry 4.0: Challenges and Innovations

TL;DR

This paper reviews recent innovations in system reliability engineering within Industry 4.0, highlighting new methodologies like AI prognostics, digital twins, and IoT, while discussing challenges such as data management and system complexity.

Contribution

It provides a comprehensive review of recent technological advancements and challenges in reliability engineering for Industry 4.0, emphasizing real-time data and adaptive methodologies.

Findings

AI-driven prognostics improve failure prediction accuracy

Digital twins enable real-time system monitoring

Condition-based maintenance reduces downtime and costs

Abstract

In the era of Industry 4.0, system reliability engineering faces both challenges and opportunities. On the one hand, the complexity of cyber-physical systems, the integration of novel numerical technologies, and the handling of large amounts of data pose new difficulties for ensuring system reliability. On the other hand, innovations such as AI-driven prognostics, digital twins, and IoT-enabled systems enable the implementation of new methodologies that are transforming reliability engineering. Condition-based monitoring and predictive maintenance are examples of key advancements, leveraging real-time sensor data collection and AI to predict and prevent equipment failures. These approaches reduce failures and downtime, lower costs, and extend equipment lifespan and sustainability. However, it also brings challenges such as data management, integrating complexity, and the need for fast and accurate models and algorithms. Overall, the convergence of advanced technologies in Industry 4.0 requires a rethinking of reliability tasks, emphasising adaptability and real-time data processing. In this chapter, we propose to review recent innovations in the field, related methods and applications, as well as challenges and barriers that remain to be explored. In the red lane, we focus on smart manufacturing and automotive engineering applications with sensor-based monitoring and driver assistance systems.