Industrial Digital Twin BIDTs for AI-Validated Asset Models
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Solution Overview
Problem
Industrial automation systems face challenges in collecting and formatting vast amounts of unstructured data from various industrial devices into meaningful presentations, requiring developers to define the meaning of each data item, which is burdensome and inefficient.
Innovation Solution
The implementation of Basic Information Data Types (BIDTs) - a set of structured data types including State, Rate, Odometer, and Event types - within industrial devices, allowing users to define associations and metadata, enabling external systems to discover and contextualize data for graphical presentations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually define the meaning of each data item from industrial devices, then data can be presented meaningfully, but the process becomes burdensome and inefficient
Solution Approach 1:
The patent creates a digital twin that copies the structure and data relationships of the physical industrial system. This digital replica automatically captures data meanings and relationships without requiring manual definition, thereby improving data integration efficiency while reducing developer burden
Solution Approach 2:
The system performs preliminary actions by automatically discovering data tags, relationships, and hierarchies from industrial devices before data presentation is needed. This automated preliminary data modeling eliminates the manual definition process and enables efficient data integration
2Quantity of substance
If vast amounts of unstructured data are collected from various industrial devices, then comprehensive data is available, but formatting and contextualizing this data becomes complex
Solution Approach 1:
The patent enforces homogeneous data structures by requiring all data tags to conform to basic information data types with standardized hierarchies and relationships. This standardization enables automated processing and contextualization of vast data volumes without increasing formatting complexity
Solution Approach 2:
The system changes data parameters by automatically inferring data tag properties, relationships, and contextual meanings from the digital twin model. This automated parameter assignment transforms unstructured data into contextualized information without manual intervention
3Stability of the object's composition
If a digital twin model is created with hierarchical elements and data tags, then data organization is improved, but the model requires validation to ensure accuracy
Solution Approach 1:
The patent implements feedback mechanisms where the AI engine continuously validates the digital twin model against actual industrial device data. This automated validation provides feedback on model accuracy and identifies discrepancies, ensuring data organization integrity without manual validation efforts
Solution Approach 2:
The system performs self-service validation where the digital twin model automatically verifies its own structure, data tag definitions, and relationships against the physical system it represents. This self-validation capability maintains organizational stability while eliminating external validation complexity
Data Source
AI summary
Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.