AI Alarm Rationalization Using Urgency and Severity Scoring
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Solution Overview
Problem
Traditional alarm rationalization processes in industrial plants are inefficient and inaccurate, leading to misrepresentative priority classifications for alarms, which can result in process upsets, plant shutdowns, and safety incidents.
Innovation Solution
An artificial intelligence-based alarm rationalization system that analyzes alarm system inputs and executes rationalizing actions based on a predetermined philosophy, considering urgency multipliers, consequence values, and severity scores to modify alarm priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alarm rationalization processes are used to determine alarm priority based solely on consequences, then the process is simple to execute, but the accuracy and reliability of alarm priority classification deteriorates
Solution Approach 1:
The patent transforms the alarm rationalization process from a simple consequence-based classification into a multi-parameter evaluation system that incorporates consequence values, severity scores, and urgency multipliers. This parameter expansion enables more accurate alarm priority classification by considering multiple dimensions of alarm impact rather than relying on a single factor.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between raw alarm data and priority classification decisions. This intermediary processes alarm inputs through machine learning models to generate rationalized priority assignments, bridging the gap between simple consequence assessment and accurate priority determination.
2Reliability
If traditional alarm rationalization processes are used, then the implementation is straightforward, but the reliability of alarm system performance deteriorates due to misrepresentative classifications
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously learns from alarm outcomes and operator responses. The system refines its priority classifications based on actual alarm performance data, creating a closed-loop system that improves reliability over time while managing complexity through adaptive learning rather than static complex rules.
Solution Approach 2:
The patent enables the alarm system to self-optimize through automated AI-driven rationalization. The system independently analyzes alarm patterns, adjusts priority classifications, and identifies rationalization opportunities without requiring manual intervention for each alarm, thereby improving reliability while containing operational complexity.
3Measurement precision
If comprehensive alarm rationalization is performed to improve accuracy, then alarm priority classification accuracy improves, but the time and resources required increase
Solution Approach 1:
The patent performs preliminary alarm rationalization during system setup and configuration phases. The AI system pre-analyzes alarm configurations, assigns initial priority classifications, and identifies rationalization opportunities before full operational deployment. This preliminary action reduces the time required for ongoing rationalization while maintaining high classification accuracy.
Solution Approach 2:
The patent implements continuous, automated AI-driven alarm rationalization that operates continuously in the background during normal system operation. Rather than requiring periodic manual rationalization campaigns that consume significant time, the system continuously refines alarm priorities and identifies optimization opportunities, distributing the computational workload over time to minimize impact on operational processes.
Data Source
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AI summary
Rationalizing an alarm system (10) within an industrial plant (111), includes an alarm system computer (106) in communication with one or more alarm system databases (102). The alarm system computer (106) is configured to execute a machine-learned model (103) to analyze at least one of alarms (101) data and process data received from the alarm system databases (102), to output, a current state of the alarm system (10) within the industrial plant (111). A predetermined alarm system philosophy (104) for the plant is provided as input, and the machine-learned model (103) identifies and executes one or more rationalizing actions for the alarm system (10) based at least on the current state and the predetermined alarm system philosophy (104) to output a rationalized future state of the alarm system (10). At least one of the one or more rationalizing actions comprises modifying an alarm priority within the alarm system (10) based at least on an urgency multiplier and a severity score or consequence value.