How Digital Twin Work?
With the above piece of information, you might have sketched an idea in your mind about the working of a “Digital Twin”. By now, after getting an approximate idea of a digital twin, you might have realized that to create a digital twin, we need physical data, virtual data and the interaction data between the two to map them together to make a digital replica of the system. Now, the question here arises is :
How is all this data collected in Digital Twin?
When it comes to discussing about working of a Digital Twin, we can only start by finding an answer to this question. For the creation of a digital twin of any system, the engineers collect and synthesize data from various sources including physical data, manufacturing data, operational data and insights from analytics software. The sensors are connected to the physical product that helps to collect data and send it back to the digital twin, and their interaction helps to optimize the product’s performance using a maintenance team.
- Engineers integrate Internet Of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Software Analytics with Spatial Network Graphs.
- The integration aims to gather relevant information and map it into a physics-based virtual simulating model.
- Analytics are applied to these models to extract performance characteristics of the physical asset.
- Seamless exchange of data is crucial for devices, including digital twins, to facilitate optimal analysis.
- Digital twins continuously update themselves from multiple sources, representing near real-time status, working condition, or position.
- Learning systems within digital twins use data from various sources, including sensors, human experts, similar machines, and the broader systems/environment.
- Past machine usage data is incorporated into the digital model.
- Analytics, such as environmental conditions and interaction analytics with other devices, are applied to detect anomalies and understand the lifecycle of the physical counterpart.
- Digital twins determine optimal processes to enhance key performance metrics and provide long-term forecasts for business optimization.
- The overall goal is to improve business outcomes by leveraging insights from the digital twin’s continuous learning and analysis.
What is a Digital Twin?
Have you ever crafted a machine? If so, envision the iterative process it took to achieve flawless functionality. We understand that the journey likely involved numerous attempts, a common challenge faced not only by you but by every manufacturer. Defects in specific fragments can lead to nonfunctionality, prompting dismantling, identification of the faulty part, and starting anew.
Ever wished you could predict a machine’s performance before assembly? Imagine simulating it on your desktop, replicating real-world behavior from micro-atomic to macro-geometric levels. This possibility is realized through a “Digital Twin.” The future of industrial services revolves around accurately predicting physical assets through their Digital Twins.