Industrial Knowledge Graph Contextualization for Asset Issue Detection
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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 that generates knowledge graphs based on aggregated operational technology data from various sources, using contextualization rules to provide insights and automate actions for asset management, supported by an AI-driven cognitive advisor for optimizing asset performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analytics methods are used with human interaction, then data can be analyzed, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables automated self-service through the knowledge graph that automatically ingests data from multiple sources, contextualizes it using predefined rules, and generates insights without human intervention. The cognitive advisor autonomously performs data analysis, issue identification, and resolution recommendations, eliminating the need for manual specialized worker involvement in each analysis cycle.
Solution Approach 2:
The patent replaces the mechanical human interaction process with an automated computational system. The knowledge graph infrastructure substitutes manual data collection, integration, and analysis with automated data ingestion from multiple sources, rule-based contextualization, and algorithmic insight generation, dramatically improving efficiency while reducing time loss.
2Loss of information
If data from multiple systems and applications is integrated, then comprehensive asset insights can be obtained, but determining inter-relationships becomes difficult and time-consuming
Solution Approach 1:
The knowledge graph serves as an intermediary layer between multiple data sources and the analysis system. It automatically ingests data from diverse sources including asset management systems, maintenance systems, and operational technology systems, then uses contextualization rules to establish inter-relationships without requiring manual complexity management. This intermediary infrastructure handles the complexity of integrating data from multiple systems while maintaining complete asset information.
3Productivity
If limited time is spent on data modeling, then resource consumption is reduced, but insights quality deteriorates
Solution Approach 1:
The system performs preliminary action by pre-configuring contextualization rules and data models during system setup. These predefined rules establish the framework for data relationships and insights generation in advance, allowing the system to automatically generate high-quality insights without requiring extensive time-consuming modeling for each analysis task. The preliminary configuration enables rapid, resource-efficient analysis while maintaining insight quality.
Data Source
AI summary
Various embodiments described herein relate to providing and/or employing a knowledge graph related to one or more assets. In this regard, a request to generate knowledge graph data 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, aggregated operational technology data is obtained based on the asset descriptor and from one or more data sources associated with the one or more assets. Furthermore, the aggregated operational technology data is contextualized, based on configuration data for the one or assets and a set of contextualization rules for the one or more data sources, to generate the knowledge graph data. The knowledge graph data is also allocated within a knowledge graph data structure configured for the one or more assets.