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’.

数字双胞胎 (DT) 正在成为各种领域中网络物理系统 (CPS) 的运行时建模和性能预测的极有前景的范例。尽管存在几种不同的 DT 定义和工业应用，从纯可视化 3D 模型到预测性维护工具，但在本文中，我们专注于数据驱动的评估和预测关键可靠性属性（如安全性）。为此，我们引入了一个基于自主系统的概念框架来托管基于结构化和系统化方法的 DT 运行时模型。我们认为，DT 和自适应之间的融合是构建更智能、有弹性和可信赖的 CPS 的关键，它可以自我监控、自我诊断并最终自我修复。概念框架简化了可靠性评估，

本文是主题问题“走向共生自治系统”的一部分。