The rise of the Internet of Things (IoT) shows no signs of slowing down, and one concept leading the charge is digital twins. As the name suggests, a digital twin is an online shadow of a physical object, process or system that enables businesses to emulate, study and optimise processes, or even simulate situations. If you manage a business and need to know how digital twins work, their applications and how to use them, here is some intel.
What is a digital twin?
A digital twin is a living, virtual replica of a physical entity, where changes in the physics of a living or dynamic object and its environment are modelled and simulated in digital form in real time. By harnessing IoT sensors and data analytics, the interaction between the thousand-fold virtual object, or twin, and the physical object is used to generate a well-informed representation of the asset’s physical and operational characteristics. There are various types of digital twin, which are detailed in the table below, created by IBM.
Types of digital twins
Component twins or Parts twins
Component twins are the building block of a digital twin: the lowest level, the simplest example of a functioning component. Parts twins are similar but are actually parts of components, though less important ones
Asset twins
When components work together, they create an asset. Asset twins generate the data streams that allow you to study the interaction of those components, yielding massive amounts of performance data that can then be analysed and transformed into actionable insights.
System or Unit twins
The next level up are system or unit twins. You can design them to allow you to ‘zoom in’ and see how various assets come together as a system. Everyone can clearly see how an entire system functions. System twins make the interaction among assets visible and may indicate performance opportunities.
Process twins
Process twins at this macro scale of magnification show how systems operate in concert with one another to comprise a complete facility. Are those systems all ‘geared up’ from the get-go to operate at maximum performance, or will hold-ups at one system inevitably drag down other systems? Process twins can elucidate the exact timing schedules that in turn affect overall performance.
Key components of digital twins:
- Physical asset: The real-world object, system or process being modelled.
- Virtual model: The digital replica that simulates the physical asset.
- Data integration: Real-time data collected from IoT sensors embedded in the physical asset.
- Analytics and simulations: Powerfully predictive algorithms and simulations that heighten the relevancy of your prediction by analysing historical data.
Benefits of digital twins
- Real-time monitoring and control: A protocol that uses a digital twin of a device has access to a live feed of data from that physical asset, which can be used for continuous monitoring and immediate mitigation if deviations are detected.
- Predictive maintenance: Data patterns help predict compromises, allowing for maintenance and repairs with minimal downtime.
- Optimised operations: Simulating ‘real life’ scenarios allows organisations to trial various scenarios and purposes to optimise processes. Without the risk of damage to existing operations, efficiency and productivity can be enhanced.
- Faster product development: Digital twins facilitate digital prototyping and virtual testing, thereby shortening the time, effort and cost of developing new products.
- Better control: Extensive data from digital twins provides decision-making insights to improve strategic planning and control operational performance.
Applications of digital twins in IoT
- Manufacturing
To ensure minimal disruption to a plant’s day-to-day operations, digital twins can act like sentries for any issues that threaten production quality. Instead of shutting down to debug a buggy programme, a digital twin can create an environment to run the programme side by side with its exact digital counterpart and find where the error is transpiring in real-time.
- Medical
In this form, digital twins provide replication of patient conditions and therefore serve for developing customised treatment programmes and for remote, real-time monitoring of the patients’ state.
- Smart cities
By making a twin from all sensors in an urban setting, digital twins let urban planners design and optimise the flow of traffic, heating and ventilation, rubbish collection and other municipal services, all of which enhance the quality of urban life.
- Energy management
Digital twins of the energy grid are created by utilities to keep tabs on performance and ensure the safety and efficiency of energy delivery.
- Automotive industry
Digital twins will keep track of a vehicle’s performance, predict when it will need maintenance, suggest required repairs and modulations in the design of next-generation vehicles to improve mileage and performance, or reduce emissions, based on how the car is driven in the real world.
Implementing digital twins: Key considerations
When you add digital twins to the infrastructure of your IoT system, take these things into account:
- Data accuracy: IoT sensors should be delivering accurate, consistent data, as any errors in input data will compromise the utility of the digital twin.
- Integration capability: Consider digital twin platforms that work well with your existing IoT systems and data sources.
- Scalability: Make sure your digital twin solution works at any and all scales: when your company grows, data volumes increase and computations become more complex – when EUDA works for enterprise-level companies as well as SMEs.
- Security: Security is a key requirement that protects your data and systems from unauthorised access and cyber-attacks.
- Cost-benefit analysis: How much does it cost to develop and maintain digital twins as compared with the potential benefits of reduced downtime, creating more efficiencies and aiding in decision-making.
Future trends in digital twins
The digital twin landscape is rapidly evolving, with several trends shaping its future:
- Integration of artificial intelligence (AI) and machine learning (ML)
More sophisticated digital twins integrate advanced AI algorithms and machine learning models, which makes the digital twin’s predictive abilities more precise, and the results of the simulation and optimisation more accurate.
- IoT and edge computing
The combination of IoT and edge computing means that much of the data processing will occur in real time, which comes with it lower latency and greater responsiveness.
- Interoperability standards
Interoperability standards that outline consistent designs and protocols will be necessary to break down barriers between digital twin platforms and IoT devices.
- Sustainability initiatives
Digital twins are being used to test and find optimal ways of reducing energy consumption and minimising waste.
Digital twins are emerging as an important application at the forefront of the IoT world, offering extremely powerful tools for enablement of real-time monitoring, predictive maintenance and operational optimisation. Given the continued growth anticipated for the Industrial Internet of Things (IIoT), it is certainly useful for tech enthusiasts to stay informed on this topic.
Article by Magda Dąbrowska, editor at WeKnow Media
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