Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However, the theoretical framework and practical implementations of digital twins (DT) are still far from this vision. Although successful implementations exist, sufficient implementation details are not publicly available, therefore it is difficult to assess their effectiveness, draw comparisons and jointly advance the DT methodology. This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. Advancements in machine learning, internet of things and big data have contributed hugely to the improvements in DT with regards to its real-time monitoring and forecasting properties. Despite this progress and individual company-based efforts, certain research gaps exist in the field, which have caused delay in the widespread adoption of this concept. We reviewed relevant works and identified that the major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics. We define the necessary components of a digital twin required for a universal reference framework, which also validate its uniqueness as a concept compared to similar concepts like simulation, autonomous systems, etc. This work further assesses the digital twin applications in different domains and the current state of machine learning and big data in it. It thus answers and identifies novel research questions, both of which will help to better understand and advance the theory and practice of digital twins.

翻译：数字双胞胎(DT)的理论框架和实际实施仍远没有达到这一愿景。尽管存在成功的实施,但在实地还存在某些研究差距,造成广泛采用这一概念的延误。我们审查了相关工作并确定延误的主要原因是缺乏普遍参考框架、共享数据对域的依赖性、对共享数据的安全关切、对数字双胞胎的依赖性、对数字双胞胎的其他技术的依赖性、以及定量指标的缺乏。我们界定了数字双胞胎在实时监测和预测特性方面所需的必要组成部分,并确定了在数字双胞胎中进行更精确的模拟,从而确认了其当前数字学和数字学的双重概念。