What we mean when we talk about digital twins

There’s a lot of hype around digital twins these days. Most companies use the phrase to mean a collection of digital assets associated with a real-world device. They use them to understand how outside forces might affect a twin’s real-world counterparts, to track problems in manufacturing processes, and even to predict outright machine failures.

The phrase was supposedly coined by NASA scientists trying to model spacecraft for the moon missions. But after visiting the Industrial IoT Lab at Finland’s Aalto University, I was shocked to realize that my understanding of digital twins was shallow at best.

I had two misconceptions. The first was pinpointing what makes a digital twin. There are a lot of companies out there using the phrase digital twin to talk about equipment monitoring programs. Basically, companies guilty of these faux digital twin software packages are using sensors on machines to show operators how those machines are performing in real time. But a fully defined digital twin is much more than a digital model that tracks how an asset is functioning.

A true digital twin has several layers of digital information, ranging from the original CAD drawings of the machine it’s twinning to detailed data around how that machine is manufactured. The goal of using a digital twin is to deeply simulate an individual machine and, by extension, predict how it will behave in certain situations or environments. As time passes, data gets added to the machine’s digital twin. The new data will reflect maintenance, prior sensor readings, breakdown history, etc.

The model of the machine should also be available offline. That means there should be a file in which the data is stored that can be accessed even without connectivity. Finally, the digital twin should also simulate the relationship of both data and metadata associated with the machine.