Digital Twins Thrive on Data Integration

Digital twins. The term was coined only ten years ago, but the concept is rapidly becoming a must-have in the manufacturing sector. Last year a Gartner poll found that 62 percent of respondents expect to be using digital twin technology by the end of this year, although only 13 percent of them were actually using it at the time. A key factor in this sudden interest is that “digital twins are delivering business value and have become part of enterprise IoT and digital strategies.”

What exactly are digital twins, and why are they getting so much attention lately? A digital twin is made up of three basic components: a physical system, a virtual representation of it, and the data that flows between them. The physical system could be an individual device, a complex machine, a whole production line, or even an entire factory. The virtual representation can be as complex as necessary to represent the system. The data connection keeps the virtual twin as closely in sync as possible with the physical twin, often tracking and updating changes in real time.

The Value and Challenge of Data Integration

A digital twin operating in isolation is useful, but the real rewards come through making connections. Data integration between multiple sub-components of a digital twin, or between multiple digital twins, is key when advancing beyond simple pilot projects. “The ability to integrate digital twins with each other will be a differentiating factor in the future, as physical assets and equipment evolve,” says the Gartner report.

There are at least three types of relationships:

  • Hierarchical, in which digital twins can be grouped together into increasingly complex assemblies, such as when the digital twins for several pieces of equipment are grouped into a larger digital twin for a whole production line.
  • Associational, where a virtual twin for one system is connected to a virtual twin in another system, in the same way that their physical counterparts are interrelated, such as wind turbines connected to a power grid.
  • Peer-to-peer, for similar or identical equipment or systems working together, like the engines of a jet airplane.

Making these connections is not always easy. A recent publication from the Industrial Internet Consortium (IIC), titled A Short Introduction to Digital Twins puts it this way, “Since the information comes from different sources, at different points in time and in different formats, establishing such relations in an automatic way is one of the major challenges in designing digital twins.”

The IIC article briefly discusses some of the technical aspects this kind of integration, such as:

  • Connectivity, the necessary first step for data integration.
  • Information synchronization keeps a virtual twin in sync with its physical twin, and among multiple connected twins, maintaining a history and/or real-time status, as required.
  • APIs allow digital twins to interact with other components of a system, and possibly with other digital twins as well.
  • Deployment between the edge and the cloud pushes data beyond the OT (Operations Technology) domain to the IT domain, that is, from the physical twin to the virtual twin.
  • Interoperability between systems from different vendors may be necessary to gain a more complete picture of the total system functionality.

Another useful resource, Digital Twin Demystified from ARC Advisory Group, identifes data connectivity, collection, tracking volume & fidelity, and ensuring the quality of real-time data as being “key challenges associated with using real-time and operational data” in digital twins.

A Good Fit

Skkynet’s software and services are well-positioned to provide the kind of data integration that digital twins require. Most data on an industrial system is available to an OPC client like the DataHub, which ensures robust connectivity. Virtually any other connection to or between digital twins, such as from legacy hardware or custom software, is possible using the DataHub’s open APIs.

Real-time data mirroring between DataHubs can handle the synchronization needed for tight correlation between the physical and virtual systems. The secure-by-design architecture of DHTP provides a proven way to connect twins across insecure networks or the Internet, even through a DMZ, to ensure the highest level of security for both the physical twin on the OT side, as well as the virtual twin on the IT side.

By supporting the most popular industrial communications protocols, and through secure, real-time data mirroring, Skkynet software and services are often used to build fully integrated systems out of components from different vendors. A recent example of this is in the TANAP project in which DataHub software was used to integrate OPC A&E (Alarm and Event) data from ABB systems with other suppliers, effectively creating a virtual digital twin of the entire 1800 km pipeline.

Digital twinning can be seen as one aspect of the whole area of digital transformation in industry. As companies move towards digitizing their operations, the ability to create a virtual twin of each component, machine, production line, or plant, and connecting that twin to their IT systems will put better control of production into the hands of managers and executives, leading to greater efficiencies. The success of this undertaking, at every step of the way, depends on secure data integration among the digital twins.