Industrial Control ML Asset for Real-Time Process Adjustment
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Solution Overview
Problem
Industrial manufacturing environments face challenges in extracting enterprise-level insights from vast data sets and automating adjustments to control programs due to the complexity and computational intensity of operational analytics, limiting the ability to leverage real-time operational data effectively.
Innovation Solution
Integration of machine learning models within industrial control code to improve functionality and autonomy, allowing for the use of pre-packaged machine learning models that can consume operational data, provide outputs to control programs, and adjust industrial processes based on predictions, with graphical interfaces for monitoring and tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are integrated into industrial control code to enable automated decision-making and optimization, then productivity and process optimization are improved, but device complexity increases
Solution Approach 1:
The patent introduces a machine learning asset as an intermediary component that sits between operational data sources and control programs. This ML asset processes complex analytics independently and provides simplified outputs to control systems, enabling automated decision-making without burdening the control code with computational complexity. The ML asset acts as a mediator that translates raw operational data into actionable insights.
Solution Approach 2:
The system segments the control architecture by separating machine learning functionality from traditional control programs. The ML asset is implemented as a distinct, modular component that can be independently trained, updated, and managed. This segmentation allows control systems to leverage advanced analytics while maintaining the simplicity and reliability of core control logic.
2Loss of information
If operational analytics are performed on enormous data sets in real time, then enterprise-level insights are improved, but loss of time increases due to computational intensity
Solution Approach 1:
The machine learning asset is trained in advance on historical operational data to learn patterns and relationships. This preliminary training phase allows the model to make rapid predictions during real-time operations without performing complex analytics on the fly. The heavy computational work is done beforehand, enabling fast real-time decision-making.
Solution Approach 2:
The patent replaces traditional mechanical analytics approaches (rule-based systems, manual analysis) with machine learning-based predictive analytics. This substitution enables the system to process enormous data sets efficiently by leveraging learned patterns rather than exhaustive computational analysis, significantly reducing processing time while maintaining insight quality.
3Adaptability or versatility
If control programs are manually edited in response to operational data, then adaptability is improved, but loss of time increases due to the difficulty and expertise required
Solution Approach 1:
The machine learning asset operates autonomously to monitor operational data, detect anomalies, and generate control adjustments without human intervention. The system self-services by automatically translating operational insights into control program modifications, eliminating the need for manual editing by experts. This autonomous operation maintains adaptability while dramatically reducing the time and expertise required for adjustments.
Solution Approach 2:
The system implements continuous feedback loops where the ML asset monitors operational data, evaluates performance, and automatically adjusts control parameters. This closed-loop feedback mechanism enables real-time adaptability by continuously learning from operational outcomes and making incremental adjustments, replacing manual editing cycles with automated adaptive control.
Data Source
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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.