PLC Programming Tags Guided by ML Correlation Analysis
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Solution Overview
Problem
Existing industrial automation environments struggle to effectively integrate machine learning models to assist in generating control programs, limiting the ability to optimize target variables in industrial processes.
Innovation Solution
A system and method that utilizes a design application and a processing application to generate and modify control programs for Programmable Logic Controllers (PLCs) by selecting and correlating program tags using machine learning models, enabling optimization of target variables through correlations identified by machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are integrated into industrial automation programming systems, then the ability to optimize target variables improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces a processing application as an intermediary layer between the design application and the PLC. This processing application handles the complex machine learning operations, correlation analysis, and tag selection, while presenting a simplified interface to the designer. The intermediary absorbs the complexity of integrating machine learning models into PLC programming, allowing the core optimization functionality to be added without directly complicating the existing PLC programming environment.
Solution Approach 2:
The patent replaces traditional manual programming methods with machine learning algorithms. Instead of requiring programmers to manually analyze process variables and determine optimal control strategies, the system uses machine learning models to automatically identify correlations between program tags and optimize target variables. This substitution of automated intelligent systems for manual mechanical programming processes adds optimization capability while managing complexity through automation.
2Reliability
If machine learning algorithms are used to analyze program tags and determine correlations, then the effectiveness of control programs improves, but the time required for program development increases
Solution Approach 1:
The system performs preliminary correlation analysis between program tags using machine learning algorithms during the design phase. By pre-computing and storing correlation data between independent variables and target variables, the system prepares optimization recommendations in advance. This preliminary action allows the PLC programming to benefit from data-driven insights without requiring time-consuming analysis during the actual programming or commissioning phase.
Solution Approach 2:
The processing application provides feedback to the design application by generating recommendations for program tag selections based on correlation analysis. This feedback mechanism allows the system to iteratively improve control program effectiveness by incorporating machine learning insights into the programming process. The feedback loop enables continuous optimization while managing development time through automated suggestion generation rather than manual trial-and-error approaches.
Data Source
AI summary
Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to optimize a target variable in an industrial automation environment. In some examples, a design application generates a control program configured and selects a program tag that represents a target variable in an industrial process. A processing application identifies a set of available program tags that represent independent variables in the industrial process and determines correlations between ones of the independent variables and the target variable. The processing application selects available program tags that represent independent variables correlated with the target variable and generates a recommendation that indicates the selected available program tags. The design application modifies the control program using the selected available program tags to optimize the target variable. The design application transfers the control program for implementation by the programmable logic controller.