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Media type:
E-Article
Title:
Digital twins as run-time predictive models for the resilience of cyber-physical systems: a conceptual framework
Contributor:
Flammini, Francesco
Published:
The Royal Society, 2021
Published in:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379 (2021) 2207, Seite 20200369
Language:
English
DOI:
10.1098/rsta.2020.0369
ISSN:
1364-503X;
1471-2962
Origination:
Footnote:
Description:
Digital twins (DT) are emerging as an extremely promising paradigm for run-time modelling and performability prediction of cyber-physical systems (CPS) in various domains. Although several different definitions and industrial applications of DT exist, ranging from purely visual three-dimensional models to predictive maintenance tools, in this paper, we focus on data-driven evaluation and prediction of critical dependability attributes such as safety. To that end, we introduce a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach. We argue that the convergence between DT and self-adaptation is the key to building smarter, resilient and trustworthy CPS that can self-monitor, self-diagnose and—ultimately—self-heal. The conceptual framework eases dependability assessment, which is essential for the certification of autonomous CPS operating with artificial intelligence and machine learning in critical applications. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.