A digital twin is a virtual model of a physical object. It spans the object’s lifecycle and uses real-time data sent from sensors on the object to simulate the behavior and monitor operations. Digital twins can replicate many real-world items, from single pieces of equipment in a factory to full installations, such as wind turbines and even entire cities. Digital twin technology allows you to oversee the performance of an asset, identify potential faults, and make better-informed decisions about maintenance and lifecycle.
What are the benefits of digital twins?
Digital twins offer users many benefits. We’ll unpack some of the following.
Improved performance
Real-time information and insights provided by digital twins let you optimize the performance of your equipment, plant, or facilities. Issues can be dealt with as they occur, ensuring systems work at their peak and reduce downtime.
Predictive capabilities
Digital twins can offer you a complete visual and digital view of your manufacturing plant, commercial building, or facility even if it is made up of thousands of pieces of equipment. Smart sensors monitor the output of every component, flagging issues or faults as they happen. You can take action at the first sign of problems rather than waiting until equipment completely breaks down.
Remote monitoring
The virtual nature of digital twins means you can remotely monitor and control facilities. Remote monitoring also means fewer people have to check on potentially dangerous industrial equipment.
Accelerated production time
You can accelerate production time on products and facilities before they exist by building digital replicas. By running scenarios, you can see how your product or facility reacts to failures and make the necessary changes before actual production.
What industries use digital twin technology?
A number of industries are increasingly using digital twins to build virtual representations of their real-world systems. Some of them include the following.
Construction
Construction teams create digital twins to better plan residential, commercial, and infrastructure projects while providing a real-time picture of how existing projects are progressing. Architects also use digital twins as part of their project planning by combining 3D modeling of buildings with digital twin technology. Commercial building managers use digital twins to monitor live and historical temperature, occupancy, and air-quality data within rooms and open spaces to improve occupant comfort.
Manufacturing
Digital twins are used across the whole manufacturing lifecycle, from designing and planning to maintaining existing facilities. A digital twin prototype allows you to monitor your equipment at all times and analyze performance data that shows how a particular part or the entirety of your plant is functioning.
Energy
Digital twins are widely used in the energy sector to support strategic project planning and optimize the performance and lifecycles of existing assets, such as offshore installations, refining facilities, wind farms, and solar projects.
Automotive
The automotive industry uses digital twins to create digital models of vehicles. Digital twins can give you insights into the physical behavior of the vehicle as well as software, mechanical, and electrical models. It is another area where predictive maintenance is valuable because a digital twin can alert a service center or user when it finds an issue with component performance.
Healthcare
Digital twins are used in the healthcare industry for several instances. These include building virtual twins of entire hospitals, other healthcare facilities, labs, and human bodies to model organs and run simulations to show how patients respond to specific treatments.
What types of digital twins are there?
There are several different digital twin types, which can often run side by side within the same system. While some digital twins replicate only single parts of an object, they’re all critical in providing a virtual representation. The most common types of digital twins are the following.
Component twins
Component twins, or parts twins, are the digital representation of a single piece of an entire system. These are essential parts of the operation of an asset, such as a motor within a wind turbine.
Asset twins
In digital twin terminology, assets are two or more components that work together as part of a more comprehensive system. Asset twins virtually represent how the components interact and produce performance data that you can analyze to make informed decisions.
System twins
A higher level of abstraction from asset twins are system twins, or unit twins. A system twin shows how different assets work together as part of a broader system. The visibility offered by system twin technology allows you to make decisions about performance enhancements or efficiencies.
Process twins
Process twins show you the digital environment of a whole object and provide insight into how its various components, assets, and units work together. For example, a digital process twin can digitally reproduce how your entire manufacturing facility is operating, bringing together all of the components within it.
How does a digital twin work?
A digital twin works by digitally replicating a physical asset in the virtual environment, including its functionality, features, and behavior. A real-time digital representation of the asset is created using smart sensors that collect data from the product. You can use the representation across the lifecycle of an asset, from initial product testing to real-world operating and decommissioning.
Digital twins use several technologies to provide a digital model of an asset. They include the following.
Internet of Things
Internet of Things refers to a collective network of connected devices and the technology that facilitates communication between devices and the cloud as well as between the devices themselves. Thanks to the advent of inexpensive computer chips and high-bandwidth telecommunication, we now have billions of devices connected to the internet. Digital twins rely on IoT sensor data to transmit information from the real-world object into the digital-world object. The data inputs into a software platform or dashboard where you can see data updating in real time.
Artificial intelligence
Artificial intelligence (AI) is the field of computer science that’s dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. Machine learning (ML) is an AI technique that develops statistical models and algorithms so that computer systems perform tasks without explicit instructions, relying on patterns and inference instead. Digital twin technology uses machine learning algorithms to process the large quantities of sensor data and identify data patterns. Artificial intelligence and machine learning (AI/ML) provide data insights about performance optimization, maintenance, emissions outputs, and efficiencies.
Digital twins compared to simulations
Digital twins and simulations are both virtual model-based simulations, but some key differences exist. Simulations are typically used for design and, in certain cases, offline optimization. Designers input changes to simulations to observe what-if scenarios. Digital twins, on the other hand, are complex, virtual environments that you can interact with and update in real time. They are bigger in scale and application.
For example, consider a car simulation. A new driver can get an immersive training experience, learn the operations of various car parts, and face different real-world scenarios while virtually driving. However, the scenarios are not linked to an actual physical car. A digital twin of the car is linked to the physical vehicle and knows everything about the actual car, such as vital performance stats, the parts replaced in the past, potential issues as observed by the sensors, previous service records, and more.
