Affiliation:
1. Institute of Technical Informatics, Graz University of Technology, 8010 Graz, Austria
2. Grenoble INP (Grenoble Institute of Engineering and Management), University Grenoble Alpes, G-SCOP, CNRS, 38000 Grenoble, France
3. Andritz Hydro GmbH, R&D Automation, 1120 Vienna, Austria
Abstract
The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.
Funder
Austrian Research Funding Agency FFG
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
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