Digital Twin Concept
The digital modelling of the physical world is one of the core concepts of the digitization of industry and the fourth industrial revolution (Industry 4.0). It foresees the development of digital representations of physical world objects and processes as a means of executing automation and control operations, based on digital operations and functionalities (i.e. in the cyber world).
Digital Models within Far-EDGE
Integration of digital models that represent the manufacturing shop floor, along with FAR-EDGE physical and logical components such as edge gateways. The FAR-EDGE reference architecture and platform design specify a set of digital models as an integral element of the FAR-EDGE automation platform. In-line with the Industry 4.0 “digital twin” concept, these digital models serve three complementary and important objectives:
- Semantic interoperability: They provide a uniform representation of the concepts and entities that comprise a FAR-EDGE deployment, which boosts semantic interoperability across diverse digital systems and physical devices. Indeed, the use of common data model provides a uniform vocabulary for describing sensors, CPS devices, SCADA systems, production systems and more.
- Information Exchange: The digital models in FAR-EDGE provide a basis for exchanging information across different FAR-EDGE deployments. This is closely related to the interoperability objective: by exchanging information in a commonly agreed format, two different FAR-EDGE deployments can become interoperable despite differences in their internal implementation.
- Digital Operations: The design and deployment of digital models is a key prerequisite for performing automation and control operations at IT (Information Technology) timescales. As part of the digitization of industry, processes and devices can be configured through IT systems. The latter systems configure and update digital models, which reflect the status of the physical world. In this way, automation and configuration operations are performed at the level of IT rather than at the level of OT (Operational Technology). However, this requires a synchronization between digital models and the status of the physical world, which can be challenging to implement.
Based on extensive review, we select and highlight AutomationML and the standards-based schemas that it comprises (such as CAEX) as a baseline approach for specifying the FAR-EDGE digital models. We extend the AutomationML/CAEX process with some additional concepts that pertain to the FAR-EDGE edge computing model to automation and distributed data analytics.
Specifically, we create a digital model that reflects concepts specified and used as part of the FAR-EDGE RA and the edge computing infrastructure of the project, such as edge gateways, data channels, measurement devices, as well as live data streams. These concepts can be blended with AutomationML and CAEX concepts as a means of putting plant models (e.g., CAEX instances) in the context of FAR-EDGE edge computing deployments.
