Exploring the concept of Cognitive Digital Twin from model-based systems engineering perspective

Abstract

Digital Twin technology has been widely applied in various industry domains. Modern industrial systems are highly complex consisting of multiple interrelated systems, subsystems and components. During the lifecycle of an industrial system, multiple digital twin models might be created related to different domains and lifecycle phases. The integration of these relevant models is crucial for creating higher-level intelligent systems. The Cognitive Digital Twin (CDT) concept has been proposed to address this challenge by empowering digital twins with augmented semantic capabilities. It aims at identifying the dynamics and interrelationships of virtual models, thus to enhance complexity management capability and to support decision-making during the entire system lifecycle. This paper aims to explore the CDT concept and its core elements following a systems engineering approach. A conceptual architecture is designed according to the ISO 42010 standard to support CDT development; and an application framework enabled by knowledge graph is provided to guide the CDT applications. In addition, an enabling tool-chain is proposed corresponding to the framework to facilitate the implementation of CDT. Finally, a case study is conducted, based on simulation experiments as a proof-of-concept.

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

“…As a specific application example, an architecture for critical safety systems is shown. In the paper [25], the authors attempted to cope with probably the most difficult challenge that arises in the application of CDT-how to improve the management of complexity and support decision making during the entire life cycle of the system. The paper explores the CDT concept and its key elements using a system engineering approach.…”

“…Source [8] identifies the following key technologies: semantic technologies (ontology engineering, knowledge graphs), model-based systems engineering technologies, PLM technologies, and industrial data management technologies (including IoT technologies, cloud computing with its extensions, natural language processing, and distributed ledger technologies). On the other hand, source [25] provides a deep elaboration on the use of a model-based systems engineering approach with accompanying technologies such as architecture modeling, simulation, semantic modeling tools, and machine learning. Virtually all the literature sources, whether explicitly or implicitly, identify semantic technologies and IoT technologies as key components for DCT implementation.…”

Exploring the Fusion of Knowledge Graphs into Cognitive Modular Production

“…Additionally, CDTs facilitate feedback loops for product life cycle management (PLM), enabling information from later life cycle stages to inform and enhance the design and creation phase of other assets. By integrating dynamic knowledge bases with digital twin models, CDTs further enable knowledge-based intelligent services for autonomous manufacturing, optimizing production processes and outcomes [14,27,30].…”

“…By structuring the data in a way that captures the relationships between different entities, a KG can improve the accuracy and reliability of predictions made by the CDT [13]. This can help manufacturers make better decisions and optimize their production processes based on the insights provided by the CDT [14]. In essence, CDTs with KGs can act as a decision support tool for manufacturers, allowing them to anticipate and mitigate potential problems, reduce downtime, and optimize their production processes [11,15,16].…”