AI Alarm Rationalization for Accurate Industrial Priority Setting

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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 AI-based alarm rationalization system that uses machine-learned models to analyze alarm system inputs and execute rationalizing actions, such as modifying alarm priorities based on urgency multipliers, consequence values, and severity scores, to optimize alarm settings and improve system effectiveness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional alarm rationalization processes are used, then alarm priority classification can be performed, but the accuracy and reliability of the classification deteriorates due to atomistic and highly variable outcomes

Engineering Contradiction:

Improvealarm priority classification accuracyVSAvoidalarm rationalization process consistency

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent replaces traditional manual alarm rationalization processes with an artificial intelligence-based system that uses machine learning models to automatically analyze alarm data, determine priorities, and generate rationalization recommendations. This substitution of mechanical human processes with automated AI systems eliminates variability and improves both accuracy and reliability of alarm priority classification.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an AI-based intermediary system that acts as a mediator between raw alarm data and rationalization decisions. This intermediary processes alarm information through trained machine learning models, applying consistent criteria and logic to generate reliable priority classifications without direct human intervention in the analysis process.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If traditional alarm rationalization processes are used, then alarm system configuration can be updated, but the time and resources required deteriorates due to intensive manual processes

Engineering Contradiction:

Improvealarm rationalization execution speedVSAvoidalarm rationalization process time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements a self-service alarm rationalization system where the AI model automatically performs analysis, generates recommendations, and can even implement changes without requiring extensive manual intervention. The system serves itself by continuously learning from alarm data and automatically optimizing alarm configurations, dramatically reducing the time and resources needed for rationalization processes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent enables continuous alarm rationalization through the AI system that operates continuously rather than through periodic manual processes. The machine learning model continuously analyzes alarm data, updates priority classifications, and refines rationalization recommendations in real-time, eliminating downtime and maintaining continuous improvement of the alarm system.

Inventive Principle:

Principle #20Continuity of useful action

3Ease of operation

If misrepresentative priority classifications are assigned, then alarm processing can proceed, but the harmful effects increase due to process upsets and safety incidents

Engineering Contradiction:

Improvealarm system operationVSAvoidprocess upset and safety incident risk

Core Design Contradiction:

Ease of operationVSObject-affected harmful factors

Solution Approach 1:

The patent implements feedback mechanisms where the AI system continuously monitors alarm performance, outcomes, and operator responses. This feedback is used to retrain and refine the machine learning models, improving the accuracy of priority classifications over time. The system learns from past alarm events and adjusts its classification logic to prevent harmful outcomes, creating a closed-loop system that continuously reduces risk.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250155864A1Artificial intelligence model to analyze process alarms and generate corresponding rationalization actions

Publication Date: 2025.05.15 SCHNEIDER ELECTRIC SYSTEMS USA INC

AI summary

Rationalizing an alarm system within an industrial plant, includes an alarm system computer in communication with one or more alarm system databases. The alarm system computer is configured to execute a machine-learned model to analyze at least one of alarms data and process data received from the alarm system databases, to output, a current state of the alarm system within the industrial plant. A predetermined alarm system philosophy for the plant is provided as input, and the machine-learned model identifies and executes one or more rationalizing actions for the alarm system based at least on the current state and the predetermined alarm system philosophy to output a rationalized future state of the alarm system. At least one of the one or more rationalizing actions comprises modifying an alarm priority within the alarm system based at least on an urgency multiplier and a severity score or consequence value.