Digital Twins and Maintenance

Reviewed by Allister Steel, Solution Engineer (April 2024)

Digital twin technology is a virtual representation of a physical object. That physical object could be a machine, product, vehicle, or process. Data is collected from the physical object and used to continuously update the digital twin. Digital twins are capable of running simulations and predicting future outcomes. For example, a digital twin can be used to predict the eventual end of an asset’s lifespan.

Digital twins also enable more efficient product development and help with predictive maintenance because they can predict machinery failure. In the future, they are expected to play a key role as the emerging field of prescriptive maintenance develops. Read on to learn more.

So, what is digital twin technology? One digital twin definition is ‘a virtual model of a physical object.’ Types of digital twins may include sources of energy such as wind turbines or power plants, propulsion systems, or an entire car, aircraft, or factory. Manufacturers also use digital twins to visualise machine tools or create virtual manufacturing cells. Digital twins always feature a two-way flow of information between the physical object and the virtual model. That model is continuously updated with real-life data. 

The digital twin can run simulations of the physical object’s behaviour. This information can then be used to make modifications to the physical system. Doing so helps optimise everything from R&D and design to manufacturing.

One of the key benefits of digital twin technology is that it’s possible to test changes to processes or designs without affecting the real-life system. Some digital twins can run lots of different simulations at once, enabling engineers to study numerous different outcomes. Engineers can then use information from the simulations to improve the design of a product or the manufacturing process. It might otherwise be very difficult to achieve this without disrupting operations or slowing down production.

Digital twins can also help speed up time to market by validating the design of a product before it exists, or identifying process failures before manufacturing starts. In this way, digital twins help identify risks, accelerate product development, and improve production processes.

During manufacturing, digital twins enable manufacturers to monitor and control product quality throughout the production process. By comparing virtual representations of products against design specifications and quality standards, manufacturers can detect defects, deviations, and anomalies early, ensuring consistent product quality and minimising the risk of defects, rework, and recalls. 

Digital twins may be used to optimise the supply chain, providing visibility and connectivity at all stages from raw materials to finished product. By integrating data from suppliers, manufacturers, and customers, digital twins enable end-to-end optimisation of supply chain processes, including inventory management, production planning, and logistics, leading to reduced lead times, improved responsiveness, and enhanced collaboration. 

The digital twin is a technology that is regarded as a key part of the fourth industrial revolution (Industry 4.0). Other key Industry 4.0 technologies include AI, machine learning, the IIoT, and Big Data. The fourth industrial revolution is marked by the convergence of physical and digital systems in a far-reaching digital transformation. With its virtual replication of machinery, entire products, or manufacturing processes, the digital twin is just one way in which this convergence is gathering momentum. 

Digital twins in manufacturing are expected to become a key enabler of accelerated product development by manufacturers. That’s because they allow engineers to make design changes and understand their impact without having to make costly and time-consuming alterations in the real world. When it comes to proving the design for a new product, the twin can also perform this job without the need to create a physical version. 

In fact, interacting with and changing the digital twin is much easier than changing a product in real life. In this way, the twin provides a risk-free product development environment. Using digital twin technology in the initial stages of product development has the potential to enable engineers to work faster and cut costs. For manufacturing of complex, high-value, bespoke machinery, where it is difficult to produce a prototype for the customer, the twin also has the ability to iron out any problems using simulation rather than having to address issues once the machine is in service. Once a product has been launched, collecting real-world data and integrating it into the digital twin model can help make improvements. 

As artificial intelligence and the IIoT continue to develop and become more widespread, the digital twin will also become more sophisticated. Digital twins may be used to simulate bigger systems such as entire cities or transport networks. As AI develops and automation trends progress, manufacturers will find themselves using digital twins that learn from experience to optimise manufacturing and offer new insights to improve product development. 

Digital twin technology in construction is on the rise, too. Digital twins for the construction sector are based on real-life building or structure data collected using sensors or 3D scanners. The twin contains every aspect of the structure and its systems in a virtual environment. Digital twins for construction provide a means of improving building information modelling, making it more detailed and comprehensive. Similar to their manufacturing counterparts, construction teams can then experiment digitally and assess outcomes without making changes that impact the actual building or construction site. 

One of the principal challenges of digital twins is maintaining the representation of the physical object correctly. The digital twin should reflect changes to the physical asset over time. That means engineers must make each digital twin include its asset’s operating condition changes and wear and tear. If the twin is not maintained effectively, then its usefulness goes down.

Future maintenance will see digitalisation continue and increasing levels of automation. For example, drones and robots will be used to conduct maintenance inspections in hazardous areas and perform tasks that are difficult or arduous for people to do. Predictive maintenance, whether via condition monitoring or the use of digital twins, is expected to become increasingly widespread. With advances in AI, maintenance systems could eventually learn from experience to self-diagnose failures in components and schedule work by automated technicians without human intervention.

In the long term, predictive maintenance is expected to evolve into prescriptive maintenance, where AI and machine learning make more and more sophisticated decisions. Prescriptive maintenance goes further than predictive systems because automation will not only predict failure, it will also identify the root cause while making recommendations for the best course of action for the engineer. 

The smart factory of the future will have a much smarter maintenance regime to match. 

Want to find out more? Develop your predictive maintenance strategy with the help of RS.