PROJECT

Digital Twin Framework

Digital Twin Framework

A Digital Twin is a digital model of an industrial asset—like a jet engine or a wind turbine. They are built by continuously collecting data off physical and virtual sensors on the asset and analyzing the data to gain unique insights about performance and operation that drive business value and outcomes.

In order for customers to realize the full value potential of Digital Twins -- including creating their own digital twins -- a robust and easy-to-use platform is needed for them to be created, live, learn, run and be deployed at an industrial scale. The Digital Twin Framework (GENIX) is built with our physical domain expertise at its core, blending digital and physical in a combination of data from the blueprints of physical assets and physical and virtual sensors on the assets themselves.

The Digital Twin Framework (GENIX) will take your data and train a model that you can deploy. To build a Digital Twin you need data about the phenomenon or event that you want to know about. If you don't know what signal tells you about the phenomenon or event, then there are tools and models that may help find the right signal. If you have operational data like sensor data and event data like maintenance records, the analytics can correlate these to build a model that can predict them in the future.

Project Impact

Digital Twin Framework (GENIX) technology enables customers to benefit from many years of GE experience to quickly understand, predict, and optimize the performance of their own unique assets. GENIX enables the creation of digital twins that represent an individual asset, an integrated system of assets, or a fleet of assets. Digital Twins running on cloud become a single source of truth for all information related to an asset, including data about past and present state, condition, and performance. In addition, early warnings and prediction could utilize not only the individual data, but also data from similar digital twins leveraging cloud. 

  • Our Expertise

    Capabilities utilized for Digital Twin Framework project

  • Physical-Digital Analytics

    Integrating deep domain expertise in core technologies with analytics that combine operational data, physical-based simulations and AI models

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  • Industrial Software

    Bringing robust software development practices to interdisciplinary engineering teams

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  • Power Electronics

    Providing power conversion and distribution solutions for the aviation, healthcare, energy and automotive sectors

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  • Machine Learning

    Developing and scaling machine learning solutions for industrial applications to allow for continuous learning, adapting and improving in dynamic operating environments

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