What you need to know about digital twins

A digital twin is a digital representation of a physical product, process, or service. Through an online connection between the physical asset and its digital counterpart, analyses can be performed to monitor health conditions and prevent potential issues.

Digital twins also enable simulations, allowing process optimization, reduction of unscheduled downtime, and other improvements. While the concept is not new, it has gained prominence with the rise of IIoT and AI technologies.

There are multiple ways to implement digital twins in industrial applications. For example, a digital twin can be created automatically when field devices are connected to IIoT ecosystems via an edge device. Alternatively, it can be generated manually by scanning a serial number, uploading a photo, or entering data through a smartphone.

In most implementations, the digital twin collects data from connected devices, analyzes it, and provides actionable insights. Development typically involves applying statistical methods, machine learning, and other advanced algorithms to interpret device performance and operational behavior.

Each asset is assigned a digital twin. This is achieved by connecting devices through a network - e.g. PROFIBUS - linked to an edge device that communicates with a cloud-base IIoT ecosystem. When the connection is established, digital twins are automatically created, ensuring accurate replication of physical assets and continuous data collection.

Depending on the IIoT platform, digital twins can be used for various purposes, such as analyzing installed bases, identifying obsolete devices, and recommending improvements.

Digitalization enables smart devices to generate large volumes of valuable data continuously. Manually interpreting this data is impractical, and ignoring it means losing critical insights into applications. A digital twin addresses this challenge by integrating with technologies such as artificial intelligence, machine learning, and IIoT, allowing simulations based on data from physical assets.

Digital twins collect and analyze information autonomously, providing actionable insights, which can help to improve processes or increase plant availability. Companies are increasingly developing open platforms to expand digital twin solutions that interpret data from connected devices. These insights are typically presented through dashboards for easy evaluation, eliminating the need for specialized data expertise

IIoT relies on smart devices that continuously deliver operational data, unlike traditional standalone systems. By connecting these devices to a cloud platform, digital twins create virtual representations. Combined with analytics tools, this approach transforms raw data into clear, actionable insights for the entire team. Applications include installed base analysis, performance improvement, and maintenance optimization.

Digital twin technology can be experienced through digital services such as Netilion Analytics, which enables users to register and organize assets in the Netilion IIoT ecosystem. This solution provides insights into the installed base, identifies obsolete devices, and highlights opportunities for standardization and process improvement.

Asset registration can be performed in several ways. The most automated method involves connecting the network to the IIoT ecosystem via an edge device, allowing digital twins to be created without manual input. Alternatively, assets can be added manually through the platform by entering device information or by using the smartphone app Netilion Scanner to scan devices, capture images, and record location details.

Once registered, the system offers analytics and insights, including asset diversity, criticality, and obsolescence, all accessible through a centralized dashboard.