Context-Aware HMI Mash-Ups for Industrial Automation
Solution Overview
Problem
Traditional Human Machine Interfaces (HMI) in industrial automation systems are limited in their ability to provide users with situation-specific information, often requiring users to navigate through multiple screens to find relevant data, and are either overly intrusive or not informative enough, leading to inefficiencies and increased costs due to excessive navigation and pre-configuration requirements.
Innovation Solution
The development of a system that dynamically superimposes situation-specific information onto the user's current view in industrial automation systems, using context-aware components to automatically provide relevant information based on user identity, role, location, and current tasks, reducing the need for explicit user requests and pre-configuration, and utilizing AI and machine learning to infer user needs and optimize data presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional HMI systems present information through preconfigured displays requiring explicit user requests, then information completeness is improved, but user interaction efficiency deteriorates due to excessive navigation
Solution Approach 1:
The system performs preliminary actions by proactively detecting alarm conditions and automatically navigating to relevant detailed screens before the user requests information. The HMI monitor continuously monitors process data and preemptively displays alarm information and navigates to detail screens, eliminating the need for users to manually navigate through multiple screens to find critical information.
Solution Approach 2:
The system serves itself by automatically detecting alarm conditions, retrieving relevant process data, and navigating to appropriate detailed screens without user intervention. The HMI system autonomously monitors its own state, identifies when information needs to be displayed, and performs navigation tasks automatically, freeing the user from manual information retrieval operations.
2Loss of information
If traditional HMI systems use pop-up displays to alert users of alarm conditions, then information delivery is improved, but user workflow continuity deteriorates due to intrusive interruptions
Solution Approach 1:
Instead of interrupting the user with pop-ups after an alarm occurs, the system performs preliminary actions by continuously monitoring process data and proactively preparing alarm information for display. The HMI system detects alarm conditions and automatically navigates to relevant screens before the user would need to request this information, delivering information seamlessly without interrupting the user's current workflow.
Solution Approach 2:
The system introduces an intermediary layer between the alarm condition and the user. The HMI monitor acts as a mediator that detects alarm conditions, retrieves relevant information, and presents it in a non-intrusive manner by automatically navigating to detail screens. This intermediary process buffers the alarm information delivery, preventing direct interruption of the user's workflow while ensuring information is delivered.
3Loss of energy
If traditional HMI systems require explicit user requests to fetch additional information, then system resource usage is improved, but information accessibility deteriorates due to pre-configuration requirements
Solution Approach 1:
The system performs preliminary actions by pre-configuring the HMI monitor to automatically detect alarm conditions and retrieve relevant process data without waiting for user requests. The system proactively fetches information about alarm conditions, process variables, and related data, making it immediately accessible when needed while maintaining efficient resource usage through targeted data retrieval based on actual alarm conditions.
Solution Approach 2:
The HMI system serves itself by automatically detecting when information is needed and fetching it without user intervention. The system autonomously monitors process data, identifies alarm conditions, and retrieves relevant information from the process control system, making information accessible without requiring users to explicitly request it while maintaining efficient resource utilization through condition-based data retrieval.
Data Source
AI summary
A visualization system that generates visual mash-ups for industrial automation includes a mash-up component that combines output from a subset of disparate sources into a common interface. The disparate sources include at least one of equipment, computers, or devices within an industrial automation environment. A visualization component generates and displays a mash-up visualization that includes information associated with the common interface.