Industrial Knowledge Graph Using Asset Models for Event Remedies
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Solution Overview
Problem
Industrial automation systems face challenges in effectively contextualizing large amounts of diverse data and building intelligence or knowledge to enable analysis, prediction, and decision-making, as they often receive limited types of data, hindering the identification of events and determination of remedies within these systems.
Innovation Solution
A unified data system applies an asset model to contextualize industrial data, generating a knowledge graph that includes nodes and edges representing entities, relationships, causes, symptoms, remedies, and recommendations, using machine learning and generative AI to identify events and provide actionable insights via a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large amounts of diverse industrial data are collected from multiple sources, then the quantity and variety of data increases, but the difficulty of contextualizing and building intelligence on the data increases
Solution Approach 1:
The patent introduces an asset model as an intermediary layer between raw industrial data and intelligence building. This asset model standardizes diverse data from multiple sources into a unified structure with defined schemas, relationships, and metadata, making the data more manageable and intelligible without requiring complex custom contextualization logic for each data source
Solution Approach 2:
The system transforms raw industrial data into contextualized data by changing its parameters and structure through the asset model. Data is converted from unstructured or semi-structured formats into standardized schemas with defined properties, relationships, and metadata, enabling consistent intelligence building across diverse data types
2Device complexity
If limited types of data (e.g., only machine data) are received from data sources, then the data processing complexity is reduced, but the ability to identify events and determine remedies is hindered
Solution Approach 1:
The asset model serves as a universal framework that can handle multiple types of industrial data (machine data, process data, quality data, maintenance data, etc.) through a single standardized structure. This multi-functional approach allows the system to process diverse data types while maintaining consistent event identification and remedy determination capabilities
Solution Approach 2:
The patent adds a new dimension to data processing by introducing the asset model layer that enriches raw data with contextual information, relationships, and metadata. This additional dimension enables better event identification and remedy determination without significantly increasing processing complexity, as the asset model provides a structured framework for organizing diverse data
Data Source
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AI summary
A system includes processing circuitry and a memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations including receiving industrial data collected from an industrial automation system during performance of an industrial automation process and applying an asset model to the industrial data to contextualize the industrial data. When executed, the instructions also cause the processing circuitry to perform operations including determining event data based on the industrial data, generating a knowledge graph based on the industrial data and the event data, identifying an event based on the event data, and providing for display via a graphical user interface (GUI), one or more remedies for the event based on the knowledge graph.