Meet Rob Tiffany
The Anatomy of the Digital Twin Instance
Now that we’ve established digital twin models and their properties, it’s time to talk about the actual digital twin instance. Rob says that we need the digital twin to always match what the real, physical entity is doing.
Typically, you’ll bind the digital twin to its physical version through an IoT platform. At this level, IoT security is paramount, so each physical entity will have a unique identifier that matches the digital twin version. There are a variety of ways to authenticate those unique identifiers, like X.509 certificates, but the main goal is to ensure that data is only being passed between trusted sources.
Once you’ve got your digital twin securely set up, you should be able to see almost every aspect of the current state of your physical machine. You can see the historical state over time and use analytics and machine learning to pick up trends and make intelligent decisions.
Additionally, applying rules to digital twins is a relatively simple way to define KPIs and derive value from the streaming data that’s coming in. You can set up basic rules so that data matches colors, for example, where green means good, yellow is a warning sign, and red means bad. After you set up basic rules like these, you can layer on more advanced analytics.
Digital Twin Subsystems and Groups
Digital twin subsystems come into play as the complexity of the machines you’re dealing with increases. A car, for example, isn’t just one thing — it’s a system of systems. You have different systems for the engine, brakes, transmission, fuel, etc.
Some of these subsystems may deserve to have their own digital twins within the larger twin, with a parent-child relationship between them. Each twin has its own properties, and you can begin to tie in causal relationships between what’s happening within one subsystem to how it affects the overall car.
Once you establish those subsystems and relationships, Rob says, you can begin building prescriptive analytics, because the next step once you detect a problem is to determine the best course of action for solving it.
From there, you can start to connect the health of an individual machine to the health of an entire factory — which is where groups come in. Groups bring together machines that interact with each other within a larger setting like a factory.
Think of an assembly line as a group of machines working together to move a product further along in its lifecycle. You’d have a digital twin of the entire line, which would have a parent relationship to the individual machines within the group. Those individual machines would then have parent relationships with their subsystems, which would have parent relationships to individual components, and so on.
An assembly line is truly a great example, Rob says, because an assembly line is a living thing, and if it goes down it can cost businesses hundreds of thousands of dollars for every hour it’s not operating. The line needs to have its own attributes based on what’s happening within its individual components and considering how those elements affect the line as a whole.
Those collections of assembly lines all together make up a giant, living digital twin called a factory — what’s known as a composite digital twin.
Tracing the Digital Thread
Once you’ve set up digital twins for your individual machines and their subsystems, groups, and even your whole factory, Rob says you can start to track what’s called the digital thread. It’s the entire lifecycle of your machine, subsystem, group, and/or factory.
With the digital thread, you can see when failures occurred, when maintenance was performed, and more to create a story of all your assets and systems. From there, you can layer on analytics to understand trends, and even use that data to craft predictive and preventative maintenance solutions.
Digital twins are a key component to bridging the gap between IoT, analytics, and machine learning, particularly in the manufacturing setting. They offer a way to remotely monitor and test connected machines, gather data on their performance, and use the insights to improve productivity and reduce waste.
We enjoyed hearing and sharing Rob’s expert presentation on digital twins since we’re digital twin practitioners ourselves. To learn more about digital twins from an engineer’s perspective, check out our guide here.