Reprint

Disruptive Trends in Automation Technology

Edited by
May 2024
206 pages
  • ISBN978-3-7258-1211-0 (Hardback)
  • ISBN978-3-7258-1212-7 (PDF)

This book is a reprint of the Special Issue Disruptive Trends in Automation Technology that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses, and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control, and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance, and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data, and artificial intelligence are all driving the transformation of conventional simulators to digital twins.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
CNN architecture; normalization techniques; intelligent fault diagnosis; vibration; continuous software engineering; DevOps; electricity market; Machine Learning; MLOps; time-series analysis; performance; control loop; monitoring; overall controller efficiency; single-input single-output; cyber security; cyber exercise; digital twin; critical systems; food supply chain; robotics; force control; stability; soft sensor; wastewater treatment; modelling; resource efficiency; exhaustive search; trajectory tracking control; powered parafoil system; linear active disturbance rejection control; twin delayed deep deterministic policy gradient; automated troubleshooting; real-time product activity detection; problem root cause analysis; machine learning; explainable AI; proactive SaaS support; cross-pollination; Juglans regia; literature review; self-compatibility; walnut blight disease; aerial pollination; artificial pollination technologies; pollination drone; high-voltage testing; surface-mount devices (SMDs); dielectric fluid hydrodynamics; SOIC package misalignment