Device-Level Industrial Data Modeling for Faster Insight Extraction
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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, as well as the identification of spurious correlations, which hinder the timely extraction of actionable insights.
Innovation Solution
A smart gateway platform leverages domain expertise to selectively model and contextualize industrial data relevant to specific business objectives, reducing the data space for AI analytics and applying pre-defined correlations, thereby streamlining the process of deriving insights and reducing data storage requirements.
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 storage requirements and processing time increase significantly
Solution Approach 1:
The patent extracts only the relevant data tags needed for specific business objectives from the complete industrial data set. The smart gateway platform uses a data model to identify and extract only those data tags that are relevant to defined business objectives, filtering out unnecessary data before storage and transmission, thereby reducing storage requirements while maintaining analytical completeness.
Solution Approach 2:
The patent segments the industrial data into organized hierarchical structures using a data model with multiple levels (e.g., machine level, component level, parameter level). This segmentation allows the system to manage large data volumes through structured organization, enabling efficient retrieval and analysis of specific data subsets without processing the entire data set.
2Loss of information
If comprehensive data analysis is performed without pre-defined correlations, then the thoroughness of analysis is improved, but the time to extract actionable insights increases
Solution Approach 1:
The patent performs preliminary action by pre-defining correlations between data tags in the data model before actual analysis occurs. The hierarchical data model establishes relationships between different data tags in advance, so when analysis is needed, the system can immediately query pre-organized data relationships rather than discovering correlations from raw data, significantly reducing insight extraction time.
3Ease of operation
If all data tags are processed equally, then the uniformity of processing is improved, but the efficiency of deriving business value decreases
Solution Approach 1:
The patent applies local quality by treating different data tags according to their specific relevance to business objectives. The system assigns different processing priorities and levels of detail to different data tags based on their importance to specific business goals, rather than applying uniform processing to all data. This allows efficient resource allocation where high-value data receives more analytical attention.
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.