The digital twin – a concept that only a few years ago was a distant vision of a digital world that existed only in research centres, today is no longer treated as a novelty, but as another necessary step in the digital transformation of companies.
A modern industry tries to address many challenges, such as operational excellence, increasing and increasing profitability, while reducing the environmental impact. These objectives can be summarised by the question: how to produce more, at a lower cost, more ecologically with the use of up-to-date technologies? To be able to address these challenges, an appropriate level of knowledge is needed along with access, on a real-time basis, to the proper information. In this case, digitisation is a means to an end. Digitisation provides the possibility of having the most detailed insight into the operation of an industrial installation. And the implementation of digital twin technology can help to achieve a higher level of productivity.
What is a digital twin? In short, it is a virtual representation of assets (components, machinery and lines) and their dynamics, with communication between the virtual and physical world of production. The physical world, i.e., the process, product or service, is reproduced in a virtual model which is an accurate copy of the original. Both these worlds, the virtual and the physical one, are connected by a link, i.e., data generated and stored in the smart components and sensors integrated with the physical element. Lessons learned from system monitoring, data analysis, simulations of various scenarios that are made in the virtual world – the digital twin – are implemented in the physical world.
There are many definitions describing the concept of digital twins, examples of them, depending on the purpose for which it has been developed, are presented in the table below.
It is clear that the concept of the digital twin is capable of generating tangible business benefits. However, it should not be overlooked that this technology also brings challenges, especially when it comes to implementation and operation. The overall priority in implementing a digital twin should be to determine the objective of this specific implementation and to specify the challenges and problems that this technology should help to solve. Enterprise strategies and actions for process control, maintenance, etc. should also include the ‘digital twin’, and a fundamental part to teach the digital twin the rules and current behaviour in the industrial plant.
Business growth and digital transformation go hand in hand with the implementation of strategies that improve operational availability, productivity and machine uptime. Reliability, is what we expect from the industry, and to achieve that, full control of resources and awareness of the condition of each asset and its components is required.
One of the many effects of the Industry 4.0 revolution is a strategic shift from a reactive to a predictive maintenance approach. Analytical solutions for predictive maintenance enable process owners to avoid unwanted, random events, and monitor assets, the entire production line or plant. When combined with the possibility to simulate behaviour, it provides companies the ability to fully optimize their operations with a maximum optimisation of their operations. It also allows for testing various variants of production development or planned investments. This is where the first, but not the only collaboration between the predictive maintenance platform and the digital twin occurs. The full representation of the system and its dynamics in real-time, allows to perform simulations of the system behaviour. This creates the possibility to test the algorithms of predictive maintenance as a part of the various scenarios that, for example, have not occurred thus far. Such simulations of behaviour of the industrial plant allow for faster and better testing of predictive maintenance solutions.
1. Opportunity to track assets, components, and processes on an ongoing basis.
2. Support to better understand problems and having the opportunity to react quickly when they occur.
3. Enabling improvements to products, operations and services.
4. Help in introducing innovative solutions within the enterprise, reducing risks that are typically associated with high-cost investments.
5. Improvement of planning for the future (use of simulations).
6. Allowing managers to trace problems that are impossible to find via traditional methodologies.
7. Making it possible to predict the type and time of failures, prior to their occurrence.
$ 18 billion – that’s how much in 2016, according to the National Institute of Standards and Technology of the United States, American entrepreneurs in the discrete manufacturing sector spent on production downtimes due to machine failures. $ 100 billion – that’s the additional cost of their lost sales due to delays and defects as […]
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