Short introduction to digital twins
What are digital twins?
A digital twin is a virtual model of a physical object or process. Such as production lines and buildings. When sensors collect data from a device, the sensor data can be used to update a “digital twin” copy of the device’s state in real time. So it can be used for things like monitoring and diagnostics.
There are different types of digital twins for designing and testing parts or products, but let’s focus more on system and process related twins.
For a simple example, you have a water heater connected to a radiator. Your virtual model gets data from the heater’s sensors and knows the temperature of the heater. The radiator on the other hand has no sensor attached to it. But the link between the heater and radiator is in your digital model. Now you can see virtually that when the heater is malfunctioning, your radiator gets colder. Not only sensors are connected to your digital twin, but manuals and other documents are also. So you can view the heater’s manual right there in the dashboard.
Industrial point of view benefits
We are living in an age when everything is connected to the internet and industrial devices are no different. Huge amounts of data is flowing from devices to different endpoints. That’s where digital twins will show its strengths by connecting all those dots to form a bigger picture about process and assets. Making it easier to understand complex structures. It’s also a two-way street, so digital twins can generate more useful data or update existing data.
Many times industrial processes consist of other processes that aren’t connected to each other. Like that lonely motor spinning without real connection to other parts of the process. Those are easily forgotten, even if it is a crucial part of the process. When complexity grows there will be even more loose ends that aren’t connected to each other.
- Predictive maintenance lowers maintenance costs.
- Productivity will improve, because reduced downtime and improved performance via optimization.
- Testing in the digital world before real world applications.
- Allows you to make more informed decisions at the beginning of the process.
- Continuous improvement through simulations.
Digital twins offer great potential for predicting the future instead of analyzing the past. Real world experiments aren’t a cost effective way to test ideas. With a digital counterpart you can cost effectively test ideas and see if you missed something important.
Quick overview of creating digital twins with AWS IoT Twinmaker
In workspace you create entities that are digital versions of devices. Those entities are connected with components that will handle data connections. Components can connect to AWS Sitewise or other data source via AWS lambda. When creating a component you define it in JSON format and it can inherit other components.
Next step is to get your CAD models uploaded to the Twinmaker. When you have your models uploaded, you can start creating 3D scenes that will visualize your digital twin. Adding visual rules like tags that change their appearance can be done in this phase.
Now digital twin is almost ready and the only thing to do is connect Grafana with Twinmaker and create a dashboard in Grafana. Grafana has a plugin for Twinmaker that helps with connecting 3D scenes and data.
There are many other tools for creating digital twins and what to use, depends on the needs.
If you are interested in how to create Digital Twins please reach out to me or the Solita Industrial team. Please also check our kickstart for Connected Factory and blog posts related to Smart and Connected Factories