Device-Level Data Contextualization for Faster Industrial Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial automation systems face challenges in deriving value from large amounts of unstructured and uncorrelated industrial data, leading to inefficiencies in data processing and storage, and often produce spurious correlations that require human verification, resulting in high costs and time delays in extracting actionable insights.
Innovation Solution
A smart gateway platform that leverages domain expertise to identify relevant industrial data subsets and applies pre-defined correlations, using smart tags with contextualization metadata to model and stream data relevant to specific business objectives, reducing the data space for AI analytics and enhancing data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all industrial data is collected and stored for analysis, then the completeness of data is improved, but the data processing time and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of industrial data needed for specific business objectives using smart tags with contextualization metadata, rather than collecting and storing all industrial data. This extraction approach maintains data completeness for relevant parameters while significantly reducing data volume for processing and storage.
Solution Approach 2:
The patent applies preliminary action by pre-defining correlations and contextualization metadata at the data collection stage, organizing data according to business objectives before analysis. This preliminary structuring eliminates the need for extensive data processing later, reducing data processing time while maintaining completeness of relevant information.
2Loss of information
If AI analytics is applied to all industrial data, then the comprehensiveness of analysis is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent extracts only the relevant subset of industrial data needed for specific business objectives using smart tags with contextualization metadata, rather than collecting and storing all industrial data. This extraction approach maintains data completeness for relevant parameters while significantly reducing data volume for processing and storage.
Solution Approach 2:
The patent applies partial action by focusing AI analytics only on the relevant subset of data identified through smart tags and contextualization metadata, rather than analyzing all industrial data. This partial analysis approach maintains comprehensiveness for business-critical parameters while significantly reducing computational resource requirements.
3Measurement precision
If domain expertise is integrated into the data model, then the accuracy of analytics is improved, but the device complexity increases
Solution Approach 1:
The patent introduces smart tags with contextualization metadata as an intermediary layer between raw industrial data and AI analytics. This intermediary structure embeds domain expertise and pre-defined correlations in a standardized format, improving analytics accuracy while managing complexity through a systematic approach rather than ad-hoc data modeling.
Data Source
AI summary
An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.