Abstract
Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.
Highlights
The concept of digital twin was first introduced in 2002 by Michael Grieves at the University of Michigan [1]
Digital twin technology has the potential to reduce the cost of system verification and testing while providing early insights into system behavior
It is a specific instance that reflects the structure, performance, health status, and mission-specific characteristics such as miles flown, malfunctions experienced, and maintenance and repair history of the physical twin; It helps determine when to schedule preventive maintenance based on knowledge of the system’s maintenance history and observed system behavior; It helps in understanding how the physical twin is performing in the real world, and how it can be expected to perform with timely maintenance in the future; It allows developers to observe system performance to understand, for example, how modifications are performing, and to get a better understanding of the operational environment; It promotes traceability between life cycle phases through connectivity provided by the digital thread; (f)
Summary
The concept of digital twin was first introduced in 2002 by Michael Grieves at the University of Michigan [1]. It is a specific instance that reflects the structure, performance, health status, and mission-specific characteristics such as miles flown, malfunctions experienced, and maintenance and repair history of the physical twin; It helps determine when to schedule preventive maintenance based on knowledge of the system’s maintenance history and observed system behavior; It helps in understanding how the physical twin is performing in the real world, and how it can be expected to perform with timely maintenance in the future; It allows developers to observe system performance to understand, for example, how modifications are performing, and to get a better understanding of the operational environment; It promotes traceability between life cycle phases through connectivity provided by the digital thread; It facilitates refinement of assumptions with predictive analytics-data collected from the physical system and incorporated in the digital twin can be analyzed along with other information sources to make predictions about future system performance; It enables maintainers to troubleshoot malfunctioning remote equipment and perform remote maintenance; It combines data from the IoT with data from the physical system to, for example, optimize service and manufacturing processes and identify needed design improvements (e.g., improved logistics support, improved mission performance); It reflects the age of the physical system by incorporating operational and maintenance data from the physical system into its models and simulations.
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