Abstract
A digital twin (DT) is a digital representation of a real-world entity, such as a device, machine, process, or complex system. The regular synchronisation between the DT and its physical counterpart offers better monitoring, improved performance, optimised maintenance, reduced downtime, and a network of connected products.
Industrial Internet of Things (IIoT) is considered as a subset of the Internet of Things (IoT) that connects various industrial assets (including machines and control systems) with the information systems and the business processes and can be utilised for optimal industrial operations. The IoT essentially connects ‘things’ to the Internet and to networks that use Internet technology. These things or items collect and share data about their internal state, the objects to which they are attached, and the environment they are in through gateways and edge computing devices. The data from such items, when fed to the DT models that combines modelling and analytics techniques using, e.g., artificial intelligence, provide information about the past and present operation and forecast the future of its physical counterpart, thus enabling the prevention of minor problems from turning into major ones and extending asset’s lifecycle.
This work introduces some of the prevailing IIoT platforms for developing DTs indicated by various studies. Information about the IIoT solutions are collected from websites and online documentation. These platforms have similar types of capabilities, with one IIoT platform performing better in one area than another. The selection of a suitable platform could be challenging as there are very few examples documented in the literature and the available information is largely from marketing materials. The future work could be focused on exploring the practical applications and limitations or scope of the IIoT platforms introduced in this study.
Industrial Internet of Things (IIoT) is considered as a subset of the Internet of Things (IoT) that connects various industrial assets (including machines and control systems) with the information systems and the business processes and can be utilised for optimal industrial operations. The IoT essentially connects ‘things’ to the Internet and to networks that use Internet technology. These things or items collect and share data about their internal state, the objects to which they are attached, and the environment they are in through gateways and edge computing devices. The data from such items, when fed to the DT models that combines modelling and analytics techniques using, e.g., artificial intelligence, provide information about the past and present operation and forecast the future of its physical counterpart, thus enabling the prevention of minor problems from turning into major ones and extending asset’s lifecycle.
This work introduces some of the prevailing IIoT platforms for developing DTs indicated by various studies. Information about the IIoT solutions are collected from websites and online documentation. These platforms have similar types of capabilities, with one IIoT platform performing better in one area than another. The selection of a suitable platform could be challenging as there are very few examples documented in the literature and the available information is largely from marketing materials. The future work could be focused on exploring the practical applications and limitations or scope of the IIoT platforms introduced in this study.
Original language | English |
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Publisher | VTT Technical Research Centre of Finland |
Number of pages | 14 |
Publication status | Published - 12 Nov 2021 |
MoE publication type | D4 Published development or research report or study |
Publication series
Series | VTT Research Report |
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Number | VTT-R-00782-21 |
Keywords
- Digital twin
- Internet of Things (IoT)
- Industrial Internet of Things (IIoT)