AI Alarm Display Mapping for Faster Industrial Root Cause Analysis
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Solution Overview
Problem
Current systems for emergency asset monitoring in industrial settings are inefficient due to manual data sorting and visualization challenges, leading to delayed responses and potential catastrophic losses, especially when alarms from remote locations or manually triggered, and lack adaptability across different display types and platforms.
Innovation Solution
A computer-based system that automatically reads asset data from a database, compares it to alarm limits, and generates displays with statistical analysis and root cause analysis, using AI and proprietary algorithms to prioritize alarms, provide historical data, and suggest actions, while adapting to different screen sizes and formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data sorting and visualization methods are used, then users can analyze alarm data, but response time is significantly delayed and productivity is reduced
Solution Approach 1:
The system automatically performs data sorting, analysis, and visualization without requiring manual user intervention. The alarm management system self-generates reports, self-correlates alarms with process data, and self-adapts to different display formats, freeing users from time-consuming manual tasks and dramatically improving response speed
Solution Approach 2:
The system pre-processes alarm data by continuously correlating alarms with process data, pre-generating visualization templates, and pre-organizing information in various formats. When an alarm occurs, the analysis and visualization are already prepared or rapidly generated from pre-established relationships, eliminating the time lag associated with manual analysis
2Adaptability or versatility
If fixed display formats are used for reports, then data presentation is consistent, but adaptability to different display types and platforms is poor
Solution Approach 1:
The display system dynamically adapts its format and content based on the target device and display characteristics. The system automatically detects the display type (desktop, mobile, tablet) and adjusts the visualization layout, data density, and graphical elements accordingly, providing optimal presentation for each platform without requiring separate configurations
Solution Approach 2:
The system employs universal visualization templates that can be rendered across multiple display platforms and formats. A single report generation mechanism produces output adaptable to various screen sizes and device types through parameter-driven formatting, eliminating the need for separate display configurations while maintaining consistency across platforms
3Loss of time
If correlation graphs are manually created and stored in user-specific folders, then graphs can be customized, but searching and retrieving graphs becomes time-consuming and inefficient
Solution Approach 1:
The system introduces a centralized search intermediary that indexes all correlation graphs and reports across the organization. This search mediator automatically catalogs graphs with metadata including process associations, alarm correlations, and usage patterns, enabling rapid retrieval through intelligent search rather than manual browsing of user-specific folders
Solution Approach 2:
The system implements feedback mechanisms that track graph usage patterns, search queries, and user interactions. This feedback is used to automatically improve search relevance, suggest relevant graphs based on current alarms and processes, and refine the organization of correlation data, making the system progressively easier to use over time
4Speed
If users manually determine equipment between measurement nodes and analyze root causes, then detailed analysis is possible, but the process is too slow for emergency situations
Solution Approach 1:
The system replaces manual mechanical analysis processes with automated computational algorithms. Machine learning models and expert systems automatically analyze process data, correlate alarms with equipment states, and determine root causes through pattern recognition, achieving both high speed and high accuracy that cannot be manually accomplished during emergencies
Solution Approach 2:
The system introduces an intelligent analysis intermediary that acts as an expert system between the alarm and the user. This intermediary automatically determines equipment involved, analyzes process correlations, and presents probable root causes with supporting evidence, performing the analytical work that would otherwise require manual expertise while operating at computer speed
Data Source
AI summary
The disclosure describes a system for the advanced delivery of information. In some embodiments, the system creates a display in response to an alarm. In some embodiments, the information on the display is a function of attribute mapping and/or analysis performed by the system. The system uses one or more of manual links, statistical analysis, correlations, maintence data, and/or historical data as tools during determination of what to display according to some embodiments. In some embodiments, the system uses one or more of these tools in conjunction with one or more of process simulators, artificial intelligence, machine learning, and/or real process feedback in the analysis to determine what to display to a user during an emergency and/or an anomalous event.