基于信息的数字孪生行为匹配模型判别
Information-Based Model Discrimination for Digital Twin Behavioral Matching
摘要：
数字孪生是一项突破性的技术，它允许根据系统及其物理定律的更新信息创建复杂物理系统的虚拟表示。然而，使数字孪生行为与真实系统匹配可能具有挑战性，因为每个孪生中的未知参数的数量。它的搜索可以使用基于优化的技术来完成，基于不同的系统数据集生成一系列模型，因此，需要一个判别标准来确定最佳的数字孪生模型。本文提出了一种基于信息论的判别准则，以确定最佳的数字孪生模型产生的行为匹配过程。采用模型的信息增益作为判别准则。Box-Jenkins模型用于为每个行为匹配结果定义模型族。所提出的方法进行了比较，与其他基于信息的度量以及$ν$间隙度量。作为一个研究案例，该判别方法被应用于数字孪生实时视觉反馈红外温度均匀性控制系统。结果表明，基于信息的方法是有用的选择一个准确的数字孪生模型代表系统之间的植物科
英文摘要：
Digital Twin is a breaking technology that allows creating virtual representations of complex physical systems based on updated information of the system and its physical laws. However, making the Digital Twin behavior matching with the real system can be challenging due to the number of unknown parameters in each twin. Its search can be done using optimization-based techniques, producing a family of models based on different system datasets, so, a discrimination criterion is required to determine the best Digital Twin model. This paper presents an information theory-based discrimination criterion to determine the best Digital Twin model resulting from a behavioral matching process. The information gain of a model is employed as a discrimination criterion. Box-Jenkins models are used to define the family of models for each behavioral matching result. The proposed method is compared with other information-based metrics as well as the $ν$gap metric. As a study case, the discrimination method is applied to the Digital Twin for a real-time vision feedback infrared temperature uniformity control system. Obtained results show that information-based methodologies are useful for selecting an accurate Digital Twin model representing the system among a family of plants
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