Digital Twin Technology: Discover the potential in a different world and apply it in the real-world

Digital… is the word that dominates the life of almost everyone and in many ways. That is why it is more than just a buzz word. It is the true reality of the world we live in. But what is Digital Twin technology? Today, with the developments of the last decade, the term digital is now used for digitalisation and the usage of huge amounts of data.

This post filters through the complexity of Digital Twin technology and explains the expertise of ANNEA when it comes to Digital Twin.

What is the Digital Twin Technology?

The Digital Twin technology stands for the digital representation of a physical object or system based on data emitted from the same physical object or system. Digital Twin technology has a huge impact on the product development processes involving engineering knowledge. Today, Digital Twin is mainly used to predict various outcomes of different scenarios. As a result, it generates value for the businesses.

How do Digital Twins work?

In order to understand and leverage the potential of the Digital Twin technology, integrating the complex systems and obtaining data on the system and the entire ecosystem is required.

Engineers or data scientists usually start the process by building a digital version (replica) of a physical object or system in a programming environment. In addition, real-word operational conditions, which affect the object or system are studied. After the study period, collected data is applied to the previously built digital version (replica) in order to simulate the real-word conditions and to compare the system’s responses in the digital environment.

Characteristics of Digital Twin

Connectivity: The very core of the Digital Twin technology relies on connectivity, which is usually provided by the IoT sensors and/or gateways.

Machine learning & democratisation of data: Considering Digital Twin as a concept of a digital technology, it is both a starting point and the result of the homogenisation of the data.

Programmable: Benefiting from the artificial intelligence and machine learning technologies, Digital Twin can be programmed due to its nature and for the needed functions.

Digital traces: The Digital Twin actually leaves traces. These traces can be used as bookmarks to solve a problem through diagnosis when it occurs.

Modularity: It stands for the customisation by design. The customisation can be applied in various ways according to the needs and area of expertise.

Things that made Digital Twin possible:

No technology emerges with the snap of a finger. These are the technologies, which enabled the rise of the Digital Twin technology.  

Simulation: Our capabilities on digital environments are increasing on a daily basis. Inspired or provided by the real-word conditions or events, it is possible to create and test various scenarios focusing on “what if?” situations.

New sources of data: Applications of Digital Twin can be fed through data acquired by continuous real-time monitoring.

Interoperability: The integration of digital technologies in the industry has increased and defined new standards of communication through IoT technology.

Visualisation: The evolution of data visualisation made it easier to understand and interpret the data. Virtual reality (VR) and augmented reality (AR) are promising technologies to evolve data visualisation even further.

Instrumentation: The costs of IoT sensors are decreasing and they are becoming more precise, resistant, powerful and physically smaller.

Platform: Currently, access to cheaper and newer technology is much easier. Increased computing power, network solutions, and data storage options are also crucial developments that enabled the Digital Twin technology.

Benefits of Digital Twin for business/industries:

Digital Twin has some similarities with other components of Industry 4.0. Improving machines’ production and increasing their efficiency can be considered the most common similarity. Due to its nature, Digital Twin offers huge benefits. It reduces business risk by simulating different scenarios in a digital environment, allowing businesses to avoid expensive costs before commissioning the machines. Businesses are also benefiting by ensuring the health and safety of their employees and business environments. The technology is reliable and accessible. It also decreases the maintenance costs by notifying the future problems and downtimes in advance.

The added value of Digital Twin:

Learning from the past: Trace back historical data focusing on the components and performance.

Becoming aware of the present: Remain up-to-date constantly with real-time data.

Predicting the future: Combine historical and real-time data to predict the future conditions of the object(s) or system.

The relationship between Digital Twin and IoT:

In our previous blog post, we have explained IoT technology and why it is a game changer. The development of IoT technology is one of the factors that enabled and brought the Digital Twin technology to this exponential level today.

The connectivity of Digital Twin is created by IoT sensors placed on physical object(s) or system that obtains data, integrates and communicates data into a digital environment, allowing the digital version (replica) to be built.

IoT technology is minimising the extensive workload immensely and in an autonomous way. Digital Twin simulations with various scenarios now include fewer complex objects, providing benefits for businesses. Moreover, the IoT integration for maximum efficiency is often optimised by Digital Twin. It also makes the process easier for designers to figure out the necessary actions to be taken in advance before the physical version of the objects or systems are built.

The relationship between Digital Twin and predictive maintenance:

In real world conditions, it is complicated and costly to establish the surroundings to test the reliability and failure behaviour of objects or systems. The Digital Twin technology is reducing the complexity and costs related to real-world applications, in order to test how objects or systems in several scenarios react to conditions. The technology can also be used for creating a failure-detection algorithm. As a result, the future health conditions of the objects or systems can be detected and actions can be taken in advance before the failures happen.

ANNEA’s unique Digital Twin technology:

The Digital Twin displayed in the ANNEA Platform is a digital representation of the physical wind turbines and their components. This is not just a 3D representation of the asset, nor a simple data visualisation as a monitoring solution. The ANNEA Digital Twin has the purpose of generating predictive value for enhancing operation and maintenance processes and performance optimisation. ANNEA’s expert team sets-up hybrid Digital Twin for each wind turbine and its main components.

By applying the Digital Twin technology, ANNEA is offering failure prediction and performance optimization. The technical solution of ANNEA uses a combination of different modelling techniques that are built up in a modular way on component and sub-component level. The modules include data driven artificial intelligence (AI), physical modelling, and normal behaviour modelling.

Contact the Expert Team of ANNEA to receive detailed information about your specific needs!

What is next?

 In our next article, you will be exploring Edge Computing, another important component of Industry 4.0.

Contact us

Email: info@annea.ai
Phone: +49 40 22866260

Follow Us

Products

ANNEA Failure Prediction

ANNEA Underperformance Detection 

ANNEA Energy Production Forecasting

ANNEA (Drone) Image Recognition

ANNEA Maintenance Planner

ANNEA Monitoring (upcoming)

© 2022 annea.ai GmbH, All Rights Reserved.

A note to our visitors

This website has updated its privacy policy in compliance with changes to European Union data protection law, for all members globally. We’ve also updated our Privacy Policy to give you more information about your rights and responsibilities with respect to your privacy and personal information. Please read this to review the updates about which cookies we use and what information we collect on our site. By continuing to use this site, you are agreeing to our updated privacy policy.

Click here to view our updated Privacy Policy

en_USEnglish