HMI Snapshot Analysis for Adaptive Industrial Control Logic
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial manufacturing environments face challenges in extracting enterprise-level insights from vast data sets and limited real-time analytics capabilities, particularly in industrial automation environments, where control programs are difficult to adjust due to the complexity and time-consuming nature of manual editing processes.
Innovation Solution
Integration of machine learning models into industrial control code to analyze operating conditions and edit control logic, utilizing components like screen capture, input, and determination components to identify issues and optimize industrial device performance, with machine learning algorithms such as convolutional neural networks for image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are integrated into industrial control code to automatically analyze operating conditions and edit control logic, then productivity and ease of operation are improved, but device complexity increases
Solution Approach 1:
A machine learning model serves as an intermediary between operational data and control program adjustments. The model receives operational data as input and automatically generates control program modifications as output, eliminating the need for manual analysis and editing by programmers. This intermediary processing layer accelerates the adjustment process while managing system complexity through automated decision-making algorithms.
Solution Approach 2:
The control system performs self-service by automatically analyzing its own operational data and generating its own control program adjustments without external human intervention. The machine learning model enables the system to self-diagnose performance issues and self-correct control logic, improving productivity while containing complexity within the automated system boundaries.
2Ease of operation
If machine learning models are used to automatically improve control programs through training and data analysis, then ease of operation is improved, but manufacturing precision requirements increase
Solution Approach 1:
The machine learning model undergoes preliminary training with extensive operational data before deployment to the industrial control system. This pre-training phase establishes high precision in identifying operating conditions and generating appropriate control adjustments. By performing this precision-critical work in advance through systematic training rather than during real-time operation, the system achieves both ease of operation and manufacturing precision.
3Adaptability or versatility
If manual editing of control programs is performed to respond to operational data, then adaptability is improved, but loss of time increases
Solution Approach 1:
The manual mechanical process of control program editing by programmers is replaced with an automated machine learning-based system. The machine learning model processes operational data and automatically generates control program adjustments, substituting human cognitive and manual work with automated computational processes. This substitution maintains full adaptability in responding to operational conditions while eliminating the time loss associated with manual editing processes.
4Manufacturing precision
If extensive data science expertise and programming knowledge are required to adjust control programs, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The machine learning model performs the complex data science analysis and control program adjustment tasks autonomously without requiring human experts. The system self-services by automatically processing operational data, identifying patterns, and generating optimized control logic. This eliminates the need for operators to possess extensive data science or programming expertise while maintaining high manufacturing precision through the model's trained analytical capabilities.
Data Source
AI summary
Various embodiments of the present technology generally relate to 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 a screen capture component configured to capture images of a human-machine interface in an industrial automation environment, wherein the one or more images include at least one visual depiction of data collected from an industrial device. The system further comprises an input component configured to provide the images to a machine learning model configured to analyze an operating condition of the industrial device. The system also comprises a determination component configured to, based on an output of the machine learning model, identify a current operating condition of the industrial device.