Asset-Centric HMI Generation From Plant Engineering Data
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Solution Overview
Problem
Current HMI systems for industrial plants are non-intuitive and difficult to use due to a process-centric design, which does not align with the asset-centric view of plant operators, requiring costly and time-consuming customization.
Innovation Solution
A system and method using machine learning to extract plant assets from engineering diagrams, build an asset hierarchy, and generate an HMI based on an ontological knowledge base, allowing for intuitive asset-centric control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a process-centric approach is used to design HMI, then the system can capture process definitions, but the interface becomes non-intuitive and difficult for operators to use
Solution Approach 1:
The patent inverts the traditional process-centric HMI design approach by implementing an asset-centric architecture. Instead of organizing the HMI around process definitions, the system organizes it around plant assets (equipment, instruments, connectors) and their relationships. This inversion allows operators to interact with the system using an asset-centric view that matches their mental model, while the backend still processes process definitions effectively.
2Adaptability or versatility
If custom HMI design is performed for each plant, then the interface can be tailored to specific needs, but the design becomes costly and time-consuming
Solution Approach 1:
The patent implements a universal asset-centric HMI framework that can be applied across different plants and processes. By defining a standardized asset model with common properties and relationships that works across multiple contexts, the system eliminates the need for extensive custom design for each plant while still allowing customization through configuration rather than redesign.
Solution Approach 2:
The system performs preliminary action by automatically extracting asset information from engineering data sources (P&ID diagrams, equipment lists, instrument indexes) and pre-building the asset model and relationships before HMI deployment. This automation of the design preparation work significantly reduces the time and cost associated with custom HMI design while maintaining adaptability to specific plant needs.
3Loss of information
If traditional HMI design methods are used, then process definitions can be captured, but specialized knowledge of plant and process engineering is required
Solution Approach 1:
The system implements self-service by automatically extracting asset information, defining asset models, and establishing relationships from engineering data sources without requiring manual intervention from specialized engineers. The automated asset extraction and model building processes capture process definitions and asset relationships independently, reducing the need for specialized design knowledge while maintaining information accuracy.
Data Source
AI summary
Systems and methods for controlling industrial process automation and control systems can automatically, through the use of machine learning (ML) models and algorithms, extract plant assets from engineering diagrams and other plant engineering data sources. The systems and methods can establish asset relationships to create a plant asset registry and build an asset hierarchy from the plant assets. The systems and methods can generate an ontological knowledge base from the plant asset hierarchy, and provide an HMI for controlling the industrial process based on the plant asset hierarchy and the ontological knowledge base.