Central to the digital transformation of the process industry are Digital Twins (DTs), virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of industrial-relevant tasks like process monitoring, predictive control or decision support. The backbone of a DT, i.e. the concrete modelling methodologies and architectural frameworks supporting these models, are complex, diverse and evolve fast, necessitating a thorough understanding of the latest state-of-the-art methods and trends to stay on top of a highly competitive market. From a research perspective, despite the high research interest in reviewing various aspects of DTs, structured literature reports specifically focusing on unravelling the utilized learning paradigms (e.g. self-supervised learning) for DT-creation in the process industry are a novel contribution in this field. This study aims to address these gaps by (1) systematically analyzing the modelling methodologies (e.g. Convolutional Neural Network, Encoder-Decoder, Hidden Markov Model) and paradigms (e.g. data-driven, physics-based, hybrid) used for DT-creation; (2) assessing the utilized learning strategies (e.g. supervised, unsupervised, self-supervised); (3) analyzing the type of modelling task (e.g. regression, classification, clustering); and (4) identifying the challenges and research gaps, as well as, discuss potential resolutions provided.

翻译：过程工业数字化转型的核心是数字孪生（DTs），即物理制造系统的虚拟副本。它通过将传感器数据与基于数据的复杂模型、基于物理的模型或二者结合，来处理诸如过程监控、预测控制或决策支持等多种工业相关任务。数字孪生的核心支撑——即支持这些模型的具体建模方法和架构框架——复杂多样且发展迅速，需要透彻理解最新的前沿方法与趋势，方能在高度竞争的市场中保持领先。从研究视角看，尽管对数字孪生各方面的综述研究兴趣高涨，但专门聚焦于剖析过程工业中用于创建数字孪生所采用的学习范式（例如自监督学习）的结构化文献报告，仍是该领域的一项新颖贡献。本研究旨在通过以下方式填补这些空白：(1) 系统分析用于创建数字孪生的建模方法（例如卷积神经网络、编码器-解码器、隐马尔可夫模型）与范式（例如数据驱动、基于物理、混合）；(2) 评估所采用的学习策略（例如监督学习、无监督学习、自监督学习）；(3) 分析建模任务的类型（例如回归、分类、聚类）；以及 (4) 识别挑战与研究缺口，并讨论已提出的潜在解决方案。