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

Antoine Tordeux, Tim M. Julitz, Isabelle Müller, Zikai Zhang, Jannis Pietruschka, Nicola Fricke, Nadine Schlüter, Stefan Bracke, Manuel Löwer

TL;DR

This chapter proposes to review recent innovations in the field, related methods and applications, as well as challenges and barriers that remain to be explored, and focuses on smart manufacturing and automotive engineering applications with sensor-based monitoring and driver assistance systems.

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.

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

TL;DR

This chapter proposes to review recent innovations in the field, related methods and applications, as well as challenges and barriers that remain to be explored, and focuses on smart manufacturing and automotive engineering applications with sensor-based monitoring and driver assistance systems.

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.

Paper Structure

This paper contains 37 sections, 22 equations, 19 figures, 1 table.

Table of Contents

  1. Introduction
  2. Innovations
  3. Prognostics
  4. Fault detection and diagnosis
  5. Remaining Useful Life Prediction and Sampling Methods
  6. System design
  7. Fail-safe, fail-degradation and fail-operational concepts
  8. Redundancy and its role in fault tolerant systems
  9. System architectures
  10. Dynamic reconfiguration in automated systems
  11. Simulation-based reliability analysis
  12. Digital twin
  13. Accelerated life testing
  14. Scenario-based simulation
  15. Challenges and development perspectives
  16. ...and 22 more sections

Figures (19)

  • Figure 1: Main sectors that contribute to advanced reliability engineering techniques during the last decades.
  • Figure 2: Cumulative number of research articles from 1997 to 2023 with the tags 'Reliability Engineering' and related keywords in Google Scholar (query made on 01.10.2024).
  • Figure 3: Transformations within the FDD system, used data-driven and model-based methods and combination strategies for hybrid approaches wilhelm2021overview
  • Figure 4: Comparison of Fail-Safe, Fail-Operational, and Fail-Degradation strategies Stolte.2022.
  • Figure 5: Electronic Control Unit Architectures at the System Level
  • ...and 14 more figures