Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

Digital Twin 是十多年前推出的，作为一种创新的包罗万象的工具，具有包括实时监控、模拟、优化和准确预测在内的感知优势。然而，数字孪生 (DT) 的理论框架和实际实施尚未大规模完全实现这一愿景。尽管在研究和工业工作中存在越来越多的成功实施，但没有足够的实施细节公开，难以全面评估其组成部分和有效性，难以进行比较，确定成功的解决方案，分享经验教训，从而共同推进和受益于 DT 方法。这项工作首先回顾了相关的 DT 研究和工业工作，重点介绍了关键的 DT 特征、不同领域的当前方法、和成功的 DT 实施，以推断关键的 DT 组件和属性，并确定当前限制和延迟广泛实施和采用数字孪生的原因。这项工作确定了这种延迟的主要原因是： DT 仍然是一个快速发展的概念；缺乏通用的 DT 参考框架，例如 DT 标准稀缺且仍在不断发展；问题和领域依赖性；共享数据的安全问题；缺乏 DT 性能指标；以及数字孪生对其他快速发展的技术的依赖。机器学习、物联网 (IoT) 和大数据的进步导致 DT 功能的显着改进，例如实时监控和准确预测。尽管取得了这些进展和个别公司的努力，该领域存在一定的研究和实施差距，迄今为止阻碍了 DT 概念和技术的广泛采用；在这项工作中也讨论了这些差距。基于对过去工作的回顾和已确定的差距，这项工作随后定义了 DT 的概念化，其中包括其组件和属性；与模拟、自主系统和优化等类似概念相比，这些也验证了 DT 作为一个概念的独特性。现实生活中的案例研究用于展示概念化的应用。这项工作讨论了 DT 的最新技术，解决了相关且及时的 DT 问题，并确定了新的研究问题，从而有助于更好地理解 DT 范式并推进 DT 及其相关技术的理论和实践。