AI Integrity Operating Window Optimization With Knowledge Graph Insights
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Solution Overview
Problem
Traditional data analytics and digital transformation for industrial assets are inefficient due to the need for human interaction and difficulty in determining inter-relationships between data from multiple systems, leading to time-consuming and resource-intensive processes.
Innovation Solution
A system utilizing a knowledge graph data structure to correlate operational technology data and provide insights, allowing for automated adjustment of operational limits and proactive issue identification through a cognitive advisor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analytics methods are used with human interaction, then insights can be obtained from asset data, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables automated self-service analytics where the cognitive advisor independently processes asset data, correlates information from multiple systems, and generates insights without requiring human analyst intervention. This automation resolves the contradiction by eliminating manual analysis time while maintaining insight quality through AI-driven correlation algorithms.
Solution Approach 2:
The patent replaces the mechanical human analysis process with an electronic cognitive advisor system that uses machine learning and knowledge graphs to automatically correlate asset data. This substitution eliminates the time-consuming manual processes while preserving analytical accuracy through sophisticated automated algorithms.
2Reliability
If data from multiple systems is analyzed to identify asset issues, then comprehensive insights are achieved, but determining inter-relationships becomes difficult and inefficient
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary data structure that standardizes and correlates information from multiple disparate systems. The knowledge graph serves as a mediator that automatically establishes inter-relationships between asset data, system data, and operational parameters, resolving the complexity of multi-system integration while improving issue identification reliability.
Solution Approach 2:
The cognitive advisor system performs multiple functions including data correlation, pattern recognition, anomaly detection, and insight generation within a single unified platform. This multi-functional approach simplifies the complexity of analyzing data from multiple systems by consolidating various analytical tasks into one versatile system.
3Reliability
If specialized workers manually monitor multiple assets, then asset issues can be identified, but the workload becomes unmanageable for large numbers of assets
Solution Approach 1:
The cognitive advisor system enables assets to monitor and report their own status automatically, eliminating the need for specialized workers to manually track each asset. The system self-identifies issues, correlates problems across assets, and generates maintenance insights autonomously, allowing one worker to effectively manage thousands of assets while maintaining monitoring reliability.
Solution Approach 2:
The patent replaces the manual mechanical monitoring process with an automated electronic system that uses machine learning algorithms to detect asset issues. This substitution dramatically increases productivity by allowing a single worker to oversee far more assets than previously possible, while the AI system maintains or even improves detection reliability through consistent automated analysis.
Data Source
AI summary
Various embodiments described herein relate to an artificial intelligence system for integrity operating window optimization related to one or more assets. In this regard, a request to obtain one or more insights related to one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aspects of aggregated operational technology data within a knowledge graph data structure are correlated to provide the one or more insights. Additionally, one or more operational limits for the one or more assets are adjusted based on the one or more insights associated with the knowledge graph data structure.