Abstract
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’.
Highlights
Critical computer-based systems, including cyberphysical systems (CPS) and the Internet of Things (IoT), combining both tangible and virtual entities, permeate our everyday lives in modern society and are becoming increasingly symbiotic with humans [1]
When Jean-Claude Laprie associated the concept of resilience to computer systems for the first time in a publication dated 2008 [12], he probably could not imagine the huge importance that the term would have gained in the following decades
We have associated the concepts of resilience, self-healing and trustworthy autonomy to the paradigm of Digital twins (DT) through run-time models embedded in the MAPE-K loop of autonomic computing
Summary
Critical computer-based systems, including cyberphysical systems (CPS) and the Internet of Things (IoT), combining both tangible and virtual entities, permeate our everyday lives in modern society and are becoming increasingly symbiotic with humans [1]. We provide a conceptual, high-level framework that summarizes recent research and future opportunities in using DT as run-time predictive models for resilience, self-healing and trustworthy autonomy of CPS. The only work that recently discussed some opportunities in this field is reported in reference [10]; in that work, the authors mainly addressed a case study of model-driven engineering connected to reflective architectural patterns, without generalizing the DT approach to CPS architectures at multiple abstraction levels Further to such generalization, based on the theory of autonomic computing for selfhealing, in this paper, we provide some hints about anomaly detection with process mining through holistic approaches that are suitable to the assessment of cooperative IoT systems-ofsystems [11].
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