Industrial Control Code Integration of Machine Learning Models
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Solution Overview
Problem
Industrial automation environments face challenges in extracting enterprise-level insights from vast amounts of operational data in real time, and existing methods for adjusting control programs during runtime are difficult and time-consuming.
Innovation Solution
Integration of machine learning models within industrial control code to improve functionality and increase autonomy of industrial control systems, allowing for real-time data analysis and adaptive control adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are integrated into industrial control code, then real-time data analysis capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between operational data sources and control decisions. Machine learning models act as mediators that process raw operational data and transform it into actionable insights that can be integrated into existing control code, enabling real-time analysis without directly complicating the core control system architecture
Solution Approach 2:
The control system is segmented into distinct functional modules: data collection components, machine learning model components, and control execution components. This segmentation allows the machine learning functionality to be added as a separate, manageable module rather than integrating it throughout the entire control system, thereby reducing overall complexity
2Ease of operation
If machine learning models are used for automated control adjustments, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The machine learning models are configured to automatically analyze operational data and generate control adjustments without requiring manual programming or intervention. The system serves itself by using historical and real-time data to autonomously optimize control parameters, improving ease of operation while the modular architecture manages the underlying complexity
Solution Approach 2:
Machine learning models are trained in advance on historical operational data to learn optimal control strategies before deployment. This preliminary training phase allows the models to be pre-configured with knowledge and patterns, enabling them to make automated adjustments during runtime without requiring complex real-time decision-making logic in the control system
3Adaptability or versatility
If control programs are adjusted during runtime using operational data, then adaptability is improved, but loss of time increases due to manual editing requirements
Solution Approach 1:
The system implements continuous feedback loops where operational data is collected, analyzed by machine learning models, and used to automatically adjust control programs in real-time. This closed-loop feedback mechanism enables the control system to adapt to changing conditions without manual intervention, improving both adaptability and response time
Solution Approach 2:
The patent replaces the manual mechanical process of editing control code with an automated computational process. Machine learning algorithms automatically generate and apply control adjustments based on operational data, substituting the time-consuming manual programming activity with automated computational analysis and code generation
Data Source
AI summary
Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises an interface component configured to display a graphical representation of a machine learning asset in an industrial automation environment, wherein the graphical representation includes a visual indicator representative of an output from the machine learning asset. The interface component is further configured to adjust the visual indicator based on the output from the machine learning asset. In addition, a process control component is configured to control an industrial process in the industrial automation environment based at least in part on the output from the machine learning asset.