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Digital twins are software representations of assets and processes that are used to understand, predict, and optimize performance in order to achieve improved business outcomes. Digital twins consist of three components: a data model, a set of analytics or algorithms, and knowledge.
Increased reliability and availability
Lower maintenance costs
Digital twins consume historical context and performance data to understand the past, use direct and indirect data to view present conditions, and apply machine learning and knowledge to predict the future. At GE Digital, we define a hierarchy of digital twins most common being: component, asset, system, and process. By knowing current context and predicting future state of a digital twin, you can effectively monitor, simulate, and control an asset or process, and optimize lifecycles whether it is online or offline.
This is a digital twin of a component of an asset, such as a bearing on a rotating piece of equipment. The component twin is typically a major sub-component that has a significant impact on the performance of the asset to which it belongs.
This is a digital twin of an entire asset, such as a motor or pump. Asset twins can be collections of and informed by component twins. Asset twins provide visibility at the equipment level.
The system or unit twin is a collection of assets that together perform a system- or network-wide function, such as an oil and gas refinery or a production line in a factory. A system twin provides visibility into a set of interdependent equipment.
Chad Stoecker, Vice President, Global Managed Services at GE Digital explains why digital twins are becoming the new normal for industrial companies. Read “From the space race to commonplace” in Connected Technology Solutions to learn more about how GE Digital defines a Digital Twin and how we are working with customers to save customers more than $1.5 billion by avoiding unplanned downtime in industries as diverse as Oil & Gas, Power Generation, Grid Transmission and Distribution, Aviation, Mining and Manufacturing.
A process twin is typically the highest level twin that provides a view into a set of activities or operations, such as a manufacturing process. The process twin can be informed by a set of asset or system twins but focuses more on the process itself rather than the equipment.
Optimizing the performance of assets to increase reliability and availability, minimize costs, and reduce operational risks.
Achieve less unplanned downtime by predicting equipment issues before they occur.
Reduce waste, improve yields and increase revenue and margins by optimizing the performance and throughput of your lines, plants and enterprise