Cross-Application Knowledge Graphs for Industrial Control Analysis
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Solution Overview
Problem
Conventional industrial process control and automation systems lack cross-leveraging of knowledge across different applications, leading to inefficiencies in operation, maintenance, and management, particularly as experienced personnel retire and less-experienced staff struggle to understand implicit relationships between various entities in the facility.
Innovation Solution
A knowledge integration tool that parses configurations of multiple applications to build input-output models, performs dependency analysis, and generates an information network model, enabling the integration of knowledge across applications and revealing implicit relationships, which can be used to enhance visualization and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple applications are used in conventional process control systems, then functional capabilities are improved, but knowledge cross-leveraging between applications deteriorates
Solution Approach 1:
The patent merges multiple application-specific knowledge bases into a unified knowledge graph that captures relationships across different applications. The system integrates process control knowledge, asset management knowledge, and maintenance knowledge into a single interconnected structure, enabling cross-leveraging of information between previously siloed applications.
Solution Approach 2:
The knowledge graph serves as a universal infrastructure that supports multiple applications simultaneously. It provides a common platform for storing, retrieving, and reasoning about knowledge across process control, asset management, and maintenance functions, allowing the same knowledge base to serve diverse functional needs.
2Stability of the object's composition
If conventional applications provide fixed human interface content, then application stability is improved, but user flexibility and understanding deteriorates
Solution Approach 1:
The system dynamically generates human interface content based on the knowledge graph and user context. Rather than fixed displays, the interface adapts to show relevant information, relationships, and insights derived from the integrated knowledge base, allowing users to explore data from multiple perspectives while the underlying application architecture remains stable.
Solution Approach 2:
The patent adds a new dimension to the user interface by incorporating knowledge relationships and contextual insights alongside traditional operational data. This creates a multi-dimensional view that combines fixed application functionality with dynamic knowledge-based enhancements, allowing users to understand implicit relationships without compromising application stability.
3Loss of information
If experienced personnel retire, then organizational knowledge is lost, but training costs and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary capture and structuring of expert knowledge into the knowledge graph before retirement occurs. By continuously integrating knowledge from experienced operators and experts into the unified knowledge base during their active careers, the system preserves institutional knowledge that can be retrieved and applied by less-experienced personnel, maintaining operational efficiency during transitions.
Data Source
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AI summary
A method includes parsing (404) configuration data of multiple applications (202, 202') associated with an industrial process control and automation system (100). The method also includes generating (406) first models associated with the applications based on the parsed configuration data. The method further includes identifying (408) relationships between the first models to thereby identify relationships between information associated with the applications. In addition, the method includes generating (410) a second model based on the identified relationships, where the second model identifies the relationships between the information associated with the applications. Parsing the configuration data could include identifying input and output variables associated with each application. The first models could include input-output models mapping the input variables versus the output variables of the applications. Generating the second model could include generating a tree or graph model that represents the relationships between the applications, where the relationships between the applications are based on variables common among multiple input-output models.