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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.
A hierarchy of systems, sub-assemblies, and components that describe the structure of the digital twin and its characteristics.
Predict, describe, and prescribe the behavior (current and future) of an asset or process via both physics and AI/ML models.
Data sources that feed analytics, subject matter expertise, historical data, and industry best practices.
Increased reliability and availability
Lower maintenance costs
What makes a digital twin?
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.
System or Unit
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.
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.
Increase revenue and margins by optimizing performance and throughput of your plants, sites, and portfolio.