What is a Digital Twin?
A digital twin is a virtual representation of a physical object, process, or system that simulates its real-world behavior in real time. It combines data, models, and analytics enhanced by first-principle models and artificial intelligence. Digital twins capture data from various sources like sensors, assets, and systems to analyze behavior, performance, and potential issues.
Key Components of a Digital Twin
Digital twins rely on accurate data collection and aggregation, AI-driven analytical capabilities, data integration into dynamic models, and multi-experience visualization layers. Continuous data streams and insights enable users to visualize and solve specific use cases, from designing capital projects to daily operations in an industrial setting.
AVEVA Digital Twins
AVEVA provides an 'intelligent twin' throughout the industrial lifecycle, seamlessly connecting engineering and operations data in a consolidated platform. Leveraging IoT devices, AI algorithms, and analytics tools, organizations can optimize efficiency, prevent downtime, and improve collaboration across teams by combining data sources for informed decision-making.
How Digital Twins Work
Digital twins gather living and raw data from automation systems, process equipment, external sources, and business indicators, enhancing it with AI for deeper insights. Real-time sync and simulation maintain accuracy, allowing for proactive decision-making. Advanced visualization tools enable stakeholders to monitor real-time performance, analyze data, and make data-driven decisions.
Digital Twins Throughout the Industrial Lifecycle
Digital twin technology supports design, operations, and optimization of industrial assets or processes. By adopting a data-centric approach, organizations can improve visibility, control, productivity, and reliability while ensuring safety and sustainability. Digital twins play a crucial role in improving efficiency, reducing rework, and enhancing overall performance.
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