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Predictive Maintenance and Digital Twin Applications: From asset twins to smart cities


Digital twin lets you monitor a manufacturing component, asset, system, or process in real-time. This enhanced monitoring capability gives a deeper understanding of what is happening on your production lines and in the wider manufacturing process.

With machine learning (ML) and inputs from expert engineers, you can also use the digital twin to identify problems before they occur and predict future outcomes. These predictions include outcomes within existing parameters as well as outcomes if those parameters change.

Digital twin technology supports other Industry 4.0 technologies, too, helping to improve overall equipment effectiveness (OEE), reduce waste, improve batch changeover times, improve product quality, enhance traceability, and more. It allows for efficient design and development, linking three-dimensional models with simulation and equipment control code emulation.

In addition, having a digital twin enables virtual troubleshooting and support, removing the physical restraints of expert engineers having to be at your location. The technology can also form the basis of customer interactive dynamic supply chains.

A digital twin allows users to investigate solutions for product lifecycle extension, manufacturing and
process improvements, and product development and prototype testing. In such cases, a digital twin can virtually represent a problem so that a solution can be devised and tested in the program rather than in the real world.

Since it can be used across a wide range of industries, from automotive to healthcare and power generation, it has already been used to solve a large number of challenges. These challenges include fatigue testing and corrosion resistance for offshore wind turbines and efficiency improvements in racing cars. Other applications have included the modeling of hospitals to determine workflows and staffing to find procedure improvements.

Some examples of typical applications for digital twins include:

  • Using predictive maintenance to maintain equipment, production lines, and facilities
  • Getting a better understanding of products by monitoring them in real-time as they are used by real customers or end-users
  • Manufacturing process optimization
  • Enhancing product traceability processes
  • Testing, validating, and refining assumptions
  • Increasing the level of integration between unconnected systems
  • Remote troubleshooting of equipment, regardless of geographical location

Digital twins are used in a wide variety of industries for a range of applications and purposes. Some notable examples include:


Digital twins can make manufacturing more productive and streamlined while reducing throughput times. There are various types of digital twins depending on the level of product magnification. The biggest difference between these twins is the area of application. It is common to have different types of digital twins co-exist within a system or process. Let’s go through the types of digital twins to learn the differences and how they are applied.

1- Component twins /Parts twins

Component twins are the basic unit of the digital twin, the smallest example of a functioning component. Parts twins are roughly the same thing but pertain to components of slightly less importance.

2- Asset twins

When two or more components work together, they form what is known as an asset. Asset twins let you
study the interaction of those components, creating a wealth of performance data that can be processed and then turned into actionable insights.

3- System or Unit twins

The next level of magnification involves systems or unit twins, which enable you to see how different assets come together to form an entire functioning system. System twins provide visibility regarding the interaction of assets and may suggest performance enhancements.

4- Process twins

Process twins, the macro level of magnification, reveal how systems work together to create an entire production facility. Are those systems all synchronized to operate at peak efficiency, or will delays in one system affect others? Process twins can help determine the precise timing schemes that ultimately influence overall effectiveness.


One example of where digital twins are used in the automotive industry is to gather and analyze operational data from a vehicle in order to assess its status in real-time and inform product improvements.

Power-generation equipment

Large engines, including jet engines, locomotive engines, and power-generation turbines, benefit tremendously from the use of digital twins, especially for helping to establish timeframes for regularly needed maintenance.


Outside of manufacturing and industry, the digital twin is used in the retail sector to model and augment the customer experience, whether at the level of a shopping center or for individual stores.


The medical sector has benefitted from digital twin in areas such as organ donation, surgery training, and de-risking of procedures. Systems have also modeled the flow of people through hospitals and track where infections may exist and who may be in danger through contact.

Disaster Management

Global climate change has had an impact across the world in recent years, yet the digital twin can help to combat this by the informed creation of smarter infrastructures, emergency response plans, and climate change monitoring.

Smart Cities

The digital twin can also be used to help cities become more economically, environmentally, and socially sustainable. Virtual models can guide planning decisions and offer solutions to the many complex challenges faced by modern cities. For example, real-time responses to problems can be informed by real-time information from digital twins to allow assets such as hospitals to react to a crisis.

Civil engineers and others involved in urban planning activities are aided significantly by the use of digital twins, which can show 3D and 4D spatial data in real-time and also incorporate augmented reality systems into built environments.


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