Industrial Alarm Prioritization Using Graph-Based Sequence Analysis
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Solution Overview
Problem
Existing alarm management systems in industrial plants struggle to efficiently identify high-priority alarms amidst a large number of alarms, leading to increased operator load, complexity, and reduced plant efficiency.
Innovation Solution
The implementation of a monitoring system that uses graph-based analysis to identify and display alarm sequences, allowing for the prediction of future alarm events and reduction of unnecessary alarms displayed to operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all alarms are displayed to system operators, then complete alarm information is provided, but operator load increases and high-priority alarms become difficult to identify
Solution Approach 1:
The alarm management system segments the alarm data by dividing all alarms into different priority levels (high, medium, low priority). This segmentation allows the system to present only relevant high-priority alarms to operators while maintaining complete alarm information in the background for full visibility when needed.
2Loss of information
If all alarms are displayed to system operators, then complete alarm information is provided, but high-priority alarms are not readily distinguishable from low-priority alarms
Solution Approach 1:
The system applies local quality by giving different visual characteristics to different priority levels of alarms. High-priority alarms are displayed with distinctive visual properties (such as color, size, or position) that make them readily distinguishable from medium and low-priority alarms, while maintaining the complete alarm information available in the system.
3Ease of operation
If simple KPI calculations are used for alarm analysis, then analysis is straightforward, but plant reliability and efficiency cannot be sufficiently improved
Solution Approach 1:
The system introduces an intermediary layer of advanced analytics between the raw alarm data and the operator interface. This intermediary layer performs complex pattern recognition, predictive analytics, and root cause analysis using machine learning algorithms, transforming raw alarm data into actionable insights that improve plant reliability while keeping the operator interface simple and easy to use.
Data Source
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AI summary
The present disclosure relates generally to alarm management in industrial plants. Industrial plants require monitoring systems to detect device failure and other events causing the plant operate at suboptimal efficiency. Plant monitoring systems alert system operators by displaying alarms corresponding to specific problems within the industrial plant. Complex industrial plants may monitor hundreds of alarms generating millions of alarm events. The number of alarms transmitted to system operators may increase to the extent that the alarms with high priority may not be readily distinguishable from the alarms with low priority. In such cases, a monitoring system may include an alarm management system to assist a system operator with identifying high priority alarms. Existing alarm management systems suffer from a number of shortcomings and disadvantages. There remain unmet needs including increasing plant reliability, reducing operator load, decreasing interface complexity, preventing future alarm events, and increasing plant efficiency. For instance, current monitoring systems may display all alarms, which requires a system operator to spend considerable time analyzing alarm data to identify high priority alarms. Furthermore, alarm analysis is limited to simple KPI calculations, such as alarm and event frequency, alarm priority, message distribution, alarm duration, and operator actions. There is a significant need for the unique apparatuses, methods, systems and techniques disclosed herein.