An Approach for Realizing Hybrid Digital Twins Using Asset Administration Shells and Apache StreamPipes
An Approach for Realizing Hybrid Digital Twins Using Asset Administration Shells and Apache StreamPipes
复制标题
使用资产管理 Shell 和 Apache StreamPipes 实现混合数字孪生的方法
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Nenad Stojanovic
中科院分区：
文献类型：
--
作者：
Michael Jacoby;Branislav Jovicic;Ljiljana Stojanović;Nenad Stojanovic
数字孪生（dt）是资产的数字表示，捕获其属性和行为。它们是工业4.0的基石之一。当前的DT标准仍在开发中，到目前为止，它们通常只允许用属性表示DT。然而，关于资产行为的知识对于正确控制和与之交互至关重要，特别是在工业生产的背景下。这种行为通常由多个不同的模型表示，使得DT中的集成和编排难以管理。在本文中，我们提出了一种将不同的DT模型交织在一起的混合DT的新方法。我们还展示了如何通过结合Fraunhofer Asset Administration Shell (AAS) Tools for Digital Twins （FAST）来实现这种方法，从而使用Apache StreamPipes创建符合工业4.0的DT，以实现和管理多个DT模型。我们的原型实现仅限于AAS元模型的一个子集，以及FAST与外部Apache StreamPipes实例之间基于拉的通信。未来的工作应该提供对AAS元模型、基于发布/订阅的通信以及其他执行环境和部署策略的全面支持。我们还介绍了如何将这种方法应用到钢铁生产行业的实际用例中。
Digital twins (DTs) are digital representations of assets, capturing their attributes and behavior. They are one of the cornerstones of Industry 4.0. Current DT standards are still under development, and so far, they typically allow for representing DTs only by attributes. Yet, knowledge about the behavior of assets is essential to properly control and interact with them, especially in the context of industrial production. This behavior is typically represented by multiple different models, making integration and orchestration within a DT difficult to manage. In this paper, we propose a new approach for hybrid DTs by intertwining different DT models. We also show how to realize this approach by combining the Fraunhofer Asset Administration Shell (AAS) Tools for Digital Twins (FAST) to create Industry 4.0-compliant DTs with Apache StreamPipes to implement and manage multiple DT models. Our prototype implementation is limited to a subset of the AAS metamodel and pull-based communication between FAST and an external Apache StreamPipes instance. Future work should provide full support for the AAS metamodel, publish/subscribe-based communication, and other execution environments as well as deployment strategies. We also present how this approach has been applied to a real-world use case in the steel production industry.