What is a digital twin?

There currently is a lot of talk running around the industry about this concept of a Digital Twin. Bentley Systems in particular have utilized this term quite a bit in the recent past in their marketing materials and visioning processes. While this is a catchy term, it’s not a particularly concrete concept. After a recent internal discussion, I felt it would be great to try to lasso our collective thoughts and opinions.

Conceptually, a Digital Twin is a virtual model of a physical asset that captures its geometry, geospatial position, and additional attributes of interest. It’s called a twin because (theoretically) any changes in the state of the physical asset are automatically (quickly?) propagated to the virtual representation.

A Worked Example of a Digital Twin

Imagine a state department of transportation responsible for tens of thousands of highway lane miles, including bridges, lighting, signs, and multiple other types of physical assets. An example of a digital twin for this type of organization might be a highway impact attenuator installed at the beginning of a concrete barrier run. The digital twin would capture its physical dimensions, geospatial position, linear reference (e.g. route and milepost), manufacturer name, model name, installation date, warranty information, and so on. Let’s say that this attenuator is a multi-cell crash unit with replaceable parts.

One day, an errant vehicle runs in to this attenuator. The driver is ok, because the attenuator has done its job and prevented impact with the much more rigid concrete barrier just a few feet downstream. We’ll say this is an attenuator with parallel rails and seven energy-absorbing cells. The first three sections are damaged and need to be replaced, but the remaining four are still in fine shape. First responders file a report, clean up, and report back to the DOT that this attenuator has been hit and needs some maintenance. This incident is recorded as a change in state of the digital twin and triggers a notice to maintenance staff that this unit requires servicing. The maintenance staff locates some spare parts, schedules a crew, and the repair is done. The repair work is captured as an additional change in state of the digital twin and the asset can then be reported again as being in a state of good repair.

Is that it?

If you think that sounds sorta underwhelming, you’re probably right. The scenario I’ve just described is along the lines of an incremental first step towards an integrated information system. In other words, this description by itself doesn’t really match all of the hype and excitement around digital twins. If we take this example a few steps forward, we can understand digital twins in terms of the current trends in the overall technology landscape.

Internet of Things and the Edge

Kids of the 80s, especially musicians, might think first of an iconic guitar player from Ireland. In the context of technology, “edge” computing is a general term for processing that takes place on the edge of the cloud. In other words, the hardware is closer to the user as opposed to being “far away” in the middle of the data center cloud.

The proliferation of IOT sensors and devices are a large driver of a general shift in focus to the edge as the next growth area in information technology. The concept of a digital twin is piggybacking on this trend. Imagine a bridge with temperature, stress, and other sensors that could report an overweight non-permitted load the second that it happens. The vision of a physical asset that is fully instrumented and able to maintain a digital representation in real time then becomes an incredibly rich information source. Rather than only having inspectors visit a bridge every two years, the bridge can report condition in real time and give advance notice of deterioration before it reaches a critical, unsafe condition.

How do I get a digital twin?

Lots of asset information is decidedly non-digital. One way to begin the transformation to digital twins is with a robust information management strategy including data collection, change management, data storage, system integration, and a host of other thorny issues. Reality Capture will no doubt be a large part of the initial generation of digital twins. The question will be how to scale up current processes to address a statewide collection effort with the attendant concerns over how long that will take and how much it will cost. Perhaps there is an opportunity to leverage big data and machine learning patterns that have been utilized to great effect in other industries such as finance. Whatever the path forward ultimately looks like, there’s no better time to get started than right now.