Automated Tag Mapping for Industrial IoT Analytics
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Solution Overview
Problem
Current approaches for mapping tag names from industrial machines to analytics are time-consuming, costly, and prone to human error, especially in modern industrial settings with thousands of tags and varying naming conventions.
Innovation Solution
The use of machine learning techniques, text and asset analytics, and automated subject matter knowledge bases to normalize and compare tag names, creating similarity measures for automated mapping, facilitated by a computerized industrial internet of things analytics platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual mapping of tag names is performed, then mapping can be completed with simple tools, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces the manual mechanical process of tag name mapping with an automated computer-based system that uses machine learning algorithms and natural language processing to automatically match equipment tag names with analytic tag names, eliminating the need for manual intervention while maintaining high accuracy
Solution Approach 2:
The patent introduces an automated mapping system as an intermediary between equipment tags and analytics, which uses normalized descriptions and similarity algorithms to bridge the gap between different naming conventions, thereby reducing both time and human error
2Ease of manufacture
If manual mapping of tag names is performed, then mapping can be done with basic tools, but human error increases
Solution Approach 1:
The patent replaces the manual mechanical process of tag name mapping with an automated computer-based system that uses machine learning algorithms and natural language processing to automatically match equipment tag names with analytic tag names, eliminating the need for manual intervention while maintaining high accuracy
Solution Approach 2:
The system incorporates feedback mechanisms where the automated mapping results can be reviewed and refined, and where the system learns from corrections to improve future mapping accuracy, thereby continuously reducing human error
3Loss of time
If automated mapping is implemented, then time and cost are reduced, but system complexity increases
Solution Approach 1:
The patent segments the automated mapping system into distinct functional modules including data normalization, similarity calculation, matching algorithms, and result validation, allowing each component to be developed and maintained independently while working together to solve the overall mapping problem
4Adaptability or versatility
If different naming conventions are used by different users, then individual preferences are accommodated, but mapping time increases
Solution Approach 1:
The patent transforms tag names from their original varied forms into a standardized normalized representation by adjusting parameters such as case, punctuation, and formatting, thereby preserving the semantic meaning while enabling automated comparison and matching across different naming conventions
Data Source
AI summary
A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.