AI-Driven Process Control Interface Clutter Reduction
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Solution Overview
Problem
Process control systems face challenges with cluttered and disorganized user interfaces that hinder operators' ability to quickly identify critical alarms and abnormalities, leading to potential system failures and device damage due to excessive information and clutter.
Innovation Solution
Employing Artificial Intelligence (AI)/Machine Learning (ML) models to categorize key features in process control system interfaces, dynamically or statically modifying images to enhance the visual perception of critical features by adjusting characteristics such as size, color, and position, and removing clutter to draw operators' attention to essential information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If more information is displayed in the user interface to provide comprehensive system monitoring, then the completeness of information is improved, but the interface becomes cluttered and harder to navigate
Solution Approach 1:
The user interface is segmented into multiple hierarchical levels: an overview screen showing summary information, and detailed screens that can be accessed for specific processes or alarms. This allows operators to view comprehensive information when needed while maintaining a clean overview interface for normal operation.
Solution Approach 2:
The interface uses spatial organization and visual hierarchy to arrange information in different dimensions. Critical alarms and abnormalities are positioned in prominent locations, while less critical information is organized in structured layouts that reduce visual clutter. The system also transitions between 2D graphical representations and detailed data views.
2Loss of time
If critical alarms and abnormalities are highlighted to improve operator response time, then the speed of detection is improved, but the visual perception requirements increase
Solution Approach 1:
The system uses color coding to indicate the severity and type of alarms and abnormalities. Critical alarms are displayed in red, warnings in yellow, and informational messages in green. This color-based classification allows operators to quickly assess the urgency of situations without complex visual analysis.
Solution Approach 2:
Critical alarms and abnormalities are given asymmetric visual treatment through larger icons, prominent positioning, and distinctive visual markers. Normal operating conditions use standardized, smaller displays. This asymmetric design creates an immediate visual hierarchy that directs operator attention to critical issues.
Data Source
AI summary
Methods and apparatus to modify user interfaces using artificial intelligence are disclosed. An example apparatus comprises instructions access first image data corresponding to a first diagram representing a process control system, the first diagram to be displayed via a user interface of the process control system, the first diagram including a first feature having a first visual characteristic and a second feature having a second visual characteristic, determine a first visual perception score associated with the first feature and a second visual perception score associated with the second feature, determine a third visual characteristic for the first feature, the third visual characteristic to increase the first visual perception score, generate second image data corresponding to the second diagram including the first and second features, the first feature having the third visual characteristic and the second feature having the second visual characteristic, and display the second diagram via the user interface.