Industrial Control Logic Analysis for Unused Variable Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Industrial automation environments face challenges in extracting insights from vast amounts of operational data due to the complexity of control code programming, limited access to runtime data, and the difficulty in identifying relevant variables among thousands of potential inputs and outputs.

Innovation Solution

Implementing a machine learning-based analysis engine within industrial programming and data science environments to analyze operational data and control logic, identify unused variables, and provide notifications and recommendations for improving control logic and data science capabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis of operational data is performed to identify relevant variables, then data science capabilities can be improved, but the process becomes extremely time-consuming and requires intimate knowledge of data science, data analytics, and process control

Engineering Contradiction:

Improveaccuracy of variable identificationVSAvoidtime required for manual data analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis with an automated machine learning-based analysis engine that uses natural language processing and pattern recognition algorithms to identify relevant variables, eliminating the need for time-consuming manual review while maintaining or improving accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary analysis engine that acts as a bridge between operational data and control logic, automatically identifying relationships and variables without requiring direct human intervention, thus reducing time loss while preserving measurement precision

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If control programmers manually edit control programs to incorporate operational data insights, then control logic accuracy improves, but the process becomes extremely difficult and time-consuming requiring intimate knowledge of multiple disciplines

Engineering Contradiction:

Improveaccuracy of control logicVSAvoidcomplexity of programming process

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent enables the control logic programming system to automatically update itself by incorporating insights from operational data analysis, eliminating the need for manual editing by programmers and reducing the complexity of the programming process while maintaining accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent performs preliminary analysis of operational data and pre-identifies relevant variables and relationships before control logic is written or updated, providing ready-to-use insights that simplify the programming process and reduce the complexity of manual editing

Inventive Principle:

Principle #10Preliminary action

3Productivity

If data scientists work with enormous amounts of operational data without access to control logic context, then data analysis can be performed, but they lack insight into the control logic or industrial automation environment

Engineering Contradiction:

Improvedata analysis capabilityVSAvoidloss of control logic context

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent merges the data analysis environment with control logic context by integrating the analysis engine with access to both operational data and control logic, allowing data scientists to perform productive analysis while retaining essential contextual information about the automation environment

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12235627B2Variable reduction for industrial automation analytics and machine learning models

Publication Date: 2025.02.25 ROCKWELL AUTOMATION TECH INC

AI summary

Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.