It took almost two decades, but CIOs all over the world are now recognizing the value of digital twins.
While the concept of digital twins has existed since the early 2000s, the demands of digital transformation and the Internet of Things (IoT) have brought them to the forefront in recent years.
But for all the talk about how digital twins are necessary for successful digital transformation, the real-world applications aren’t always clear.
Rather than getting caught up in the high-level concept of digital twins, we can think about the value as it applies to industrial use cases like wind turbines and similar scenarios.
Digital Twins for Wind Turbine Management
When wind turbines were only just emerging, the focus was more on energy production as opposed to lifecycle management for the turbines themselves. If there were issues, the scale was low enough that wind turbines could be managed physically (though the inefficiencies were recognized).
Now, wind power has grown more prominent and is only expected to expand in the coming years. And as a result, the scale at which we must manage this equipment has far-exceeded manual/physical processes.
In the age of digital transformation, we need an agile way to manage wind turbines that enables three key functions:
- The ability to identify, predict, and warn of failures/disruptions
- Optimization of the environment to mitigate problems
- Forecasts of opportunities for improvement in lifecycle management
Digital twins offer this value at scale for wind turbines. By creating a virtual model of the physical wind turbine, the equipment can be managed remotely as applications and data relay information to your analytics software.
The digital twin includes the physical characteristics of a turbine (asset and configuration data) as well as any software objects, data, and processes included in the operational lifecycle.
This means you have a perfect clone of the physical environment available whenever, wherever necessary. As you try to build out a digital transformation strategy, this agile approach to lifecycle management will save you countless hours in IT resources in addition to making your operations more cost efficient.
Digital twins allow you to streamline maintenance schedules, power distribution, and load balancing for wind turbines, specifically. But this use case extends to just about any industrial application.
The key is properly deploying your digital twins to capitalize on the promise of improved organizational effectiveness.
Edge Clouds and Digital Twins—Better Together
At their core, digital twins apply a closed-loop operational model to your industrial IoT applications. You monitor, analyze, and act based on data at the source.
The challenge is executing this model without the latency of back-and-forth communication between your back-end infrastructure and the intelligent edge. That’s why edge clouds and digital twins are better together.
When you deploy digital twins with edge clouds, you’re able to perform the critical elements of your closed-loop operational model as close to the IoT device as possible. Building that power directly into an edge device can be problematic—but having the local resources available in an edge cloud is both efficient and cost-effective.
It all comes down to finding that perfect balance between edge cloud deployments and digital twin use cases. If you want to learn more about making this happen for your industrial needs, check out our free white paper, Creating Dynamic Digital Twins with Edge Clouds.