Closed-Loop Model Management for Industrial Control Code
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Solution Overview
Problem
Industrial manufacturing environments face challenges in extracting enterprise-level insights from vast data sets and limited real-time analytics capabilities due to the complexity of operational data and the difficulty in adjusting control programs post-implementation.
Innovation Solution
Integration of machine learning models into industrial control code to enhance functionality and autonomy, allowing for real-time data analysis and automatic model updates based on performance gaps, enabling continuous optimization of industrial processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are integrated into industrial control code, then real-time data analysis capability and productivity are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent introduces a model management system as an intermediary layer between the machine learning models and the industrial control code. This mediator handles model deployment, monitoring, and updates automatically, reducing the complexity burden on the control system while maintaining real-time data analysis capabilities. The intermediary manages the computational overhead and coordinates between different system components.
Solution Approach 2:
The patent segments the control system into distinct functional modules: the industrial control code, the machine learning model, and the model management system. This segmentation allows each component to be developed, deployed, and maintained independently, managing overall system complexity while enabling real-time analytics through the integrated ML models.
2Extent of automation
If machine learning models are integrated into industrial control code, then autonomy and functionality are improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements a feedback mechanism where the model management system continuously monitors the performance of integrated machine learning models by comparing predicted outcomes with actual industrial process results. This feedback loop automatically detects performance degradation and triggers model retraining or updates, making autonomy measurable and manageable without increasing overall system complexity.
3Adaptability or versatility
If manual editing of control programs is performed, then adaptability is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent implements self-service automation where the model management system automatically monitors model performance, detects degradation, retrains models using historical data, and deploys updated models without human intervention. This maintains adaptability to changing industrial conditions while eliminating the time loss associated with manual control program editing.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models offline using historical industrial data before deployment. When performance degradation is detected, the model management system automatically retrains models using accumulated data, preparing updated models in advance before deploying them to maintain optimal performance without interrupting industrial operations.
4Adaptability or versatility
If extensive programming is used to implement machine learning tasks, then functionality is improved, but device complexity and manufacturing precision increase
Solution Approach 1:
The model management system serves as an intermediary that handles the complexity of machine learning model integration, training, and deployment. This allows industrial control code to leverage machine learning functionality without directly implementing the complex programming required for ML tasks, maintaining ease of manufacture while achieving advanced adaptability.
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 include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.