What Is Digital Twin Technology?

A digital twin is a virtual model of a physical object, process, or system. It spans the system’s lifecycle and uses real-time data sent from sensors within the system to simulate its behavior and monitor operations. Digital twins can replicate many real-world systems, from single pieces of equipment in a factory to large, complex systems, 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.

A digital twin is made up of the following technologies:

• Physical system: Businesses have a primary physical asset that they want to simulate virtually.
• Virtual model: The virtual model is the direct digital representation of the physical object or live system.
• Data ingestion: Ingestion tools and connectors ensure that sensor data and usage metrics from the physical system flow into data systems.
• Data management and analytics: Any collected operational data is processed, stored, and analyzed to help identify anomalies and trends in the asset’s lifecycle.
• Visualization tools: A visualization interface helps access the virtual environment, allowing users to explore virtual replicas of these real-world systems.
• Lifecycle management systems: Feedback loops send data between the physical asset and digital twin for optimization. Organizations may also use other integrations to let multiple digital twins share data, helping to develop digital twins at scale.

Digital twins offer users many benefits.

Improved performance

Real-time information and insights gathered from digital twins let you optimize the performance of your equipment, plant, or facilities. Issues can be dealt with before or soon after they occur, ensuring systems can support peak performance and reducing 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 before and as they happen. You can take action at the first sign of problems rather than waiting until the 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 staff 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 simulation scenarios, you can see how your product or facility reacts to failures and make the necessary changes before actual production.

A number of industries use 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.

Smart cities

Smart cities use digital twin technology to create a living digital version of urban environments. These digital twins mirror the roads, buildings, transportation networks, and public services within the space. City planners can use these city-scale digital twins to connect the physical and digital worlds, seeing how complex flows of traffic or pedestrian movement work in the physical world.

A smart city can draw from embedded sensors, IoT platforms, and real-time data across a city to reflect traffic, energy consumption, or environmental conditions in the city’s virtual twin. The digital counterpart will update automatically, keeping stakeholders informed about how they can optimize or improve the city experience and save energy.

Advanced manufacturing

In advanced manufacturing, digital twins closely model the workflows and equipment within the manufacturing processes. Product digital twins will allow engineers to evaluate performance, quality, and reliability before something goes into production.

The physical twin on the factory floor delivers data continuously, letting you watch from the virtual twin and locate opportunities to optimize steps within the process. Dips in performance related to the physical counterparts may signal where you can use predictive maintenance to avoid future scenarios in which a part fails.

Data centers

A digital twin in a data center allows you to trace the cooling systems, power levels, and server loads in each location. You can use the digital asset for experimentation, such as seeing how your complex systems would respond to an outage or major change in energy. Connecting data between the digital twin and the physical counterparts also means you can see how automating repetitive tasks would increase the efficiency of your system.

Automotive industry

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 across multiple applications. 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.

There are several different digital twin types, which can often run side by side within the same system. Here are the most common types of digital twins.

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

The next step up 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 a specific process or workflow and provide insight into how its various components, assets, and units work together. For example, a digital process twin can digitally reproduce a specific product manufacturing process within your manufacturing facility. This will include all of the components within the process, but exclude others, such as a component for manufacturing a different product.

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. The following technologies are often used in digital twins.

Internet of Things

The Internet of Things (IoT) 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 is input into a software application, often paired with a 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 can use 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 with real-world, live telemetry data. They are bigger in scale and application.

For example, consider a car simulation. A car company can model a car and run simulations to examine when parts might wear, how the car responds to brake pressure, and other new real-world scenarios. 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.

Digital twin versus digital thread

Both digital twins and digital threads are digital representations of physical objects. However, they don’t work in the same way and have several core differences that set them apart.

Digital twin technology is a digital replica of a physical system or object. It extrapolates all of the data from the physical source, such as its behavior models and performance information, and transports it to the virtual model. Having this alternative environment lets engineers and stakeholders monitor performance in real time and make optimizations.

A digital thread is the digital data lineage of product-related data across its entire lifecycle. A digital thread mirrors the flow of data in this lifecycle, starting at initial design, passing through production, and into operation. A digital thread provides insight into manufacturing processes, clarifying decision-making at every step of the process. At any moment, a team could examine the digital thread to identify whether a product’s development was going as planned. For example, a digital thread could contain the design specifications of a bolt, its testing parameters, records of defects, and other data across its lifecycle.

AWS is working with many enterprises on digital twin projects. They include some of the following.

KONE

As a global elevator and escalator leader, KONE safely moves over two billion people daily—redefining urban journeys with smart, secure technologies. KONE’s AWS-powered digital twin solution helps station managers prevent crowding before it happens, keeping people safe, moving, and informed.

Swoop Aero

Swoop Aero manages its drone fleets through digital twins based on the AWS IoT Device Shadow service. The service stores a shadow state for each drone and performs automated performance status checks against the twin to ensure the reliability of the aircraft. If a deviation is detected, it automatically logs a defect against the aircraft and the system blocks it from taking off.

John Holland

John Holland is one of Australia's leading integrated infrastructure and property companies. As part of a digital transformation, it was able to create construction digital twins, providing managers with a digital picture of their projects. AWS captures operational data for performance monitoring, environment monitoring, claims, and historical data.

AWS IoT TwinMaker helps you optimize operations and performance by creating digital twins of real-world systems. With AWS IoT TwinMaker, you can use built-in connectors or create your own connectors to easily access and use data from a variety of data sources, such as equipment sensors, video feeds, and business applications. Import your existing 3D visual models to quickly create digital twins of your facilities, processes, and equipment that update in real time with data from connected sensors and cameras, visualize insights and predictions based on the data, and raise alarms to identify when data or predictions deviate from expectations. Easily integrate these digital twins into web-based applications that allow your plant operators and maintenance engineers to monitor and improve your operations.

Get started with AWS IoT TwinMaker by creating a free AWS account today.