BMS Alarm Classification Using Dual AI Models for False Alarm Mitigation

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Solution Overview

Problem

Building Management Systems often generate false alarms, diverting operator attention and wasting time, as existing systems lack the ability to automatically differentiate between true and false alarms.

Innovation Solution

Implementing a dual AI model system comprising a false alarm AI Model and a true alarm AI Model to classify incoming alarms, with automatic classification based on consensus or operator intervention when models disagree.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional alarm systems are used, then all alarms are presented to operators for manual assessment, but this causes operator time waste and attention diversion due to false alarms

Engineering Contradiction:

Improvealarm accuracyVSAvoidoperator response time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary classification of alarms using trained AI models before presenting them to operators. The false alarm and true alarm models pre-assess incoming alarms, automatically filtering out false alarms and prioritizing true alarms for operator attention, thus saving operator time and reducing false alarm interference

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the manual mechanical assessment process with automated AI-based classification systems. The false alarm model and true alarm model use machine learning algorithms to automatically distinguish between false and true alarms, substituting operator judgment with automated intelligent systems that can rapidly process and classify alarms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Extent of automation

If automated classification systems are implemented, then false alarms can be automatically filtered, but system complexity increases due to multiple AI models and classification logic

Engineering Contradiction:

Improveautomatic alarm classificationVSAvoiddual AI model system

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The classification system is segmented into two specialized AI models: a false alarm model trained to identify false alarms and a true alarm model trained to identify true alarms. This segmentation allows each model to specialize in detecting specific alarm types, improving classification accuracy while maintaining manageable system complexity through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary classification layer between the alarm source and the operator console. This intermediary system uses the dual AI models to process alarms and determine their authenticity before presentation to operators, acting as a mediator that filters and prioritizes alarm information without requiring direct operator intervention in the classification process

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260057764A1Systems and methods for mitigating false alarms in a building management system

Publication Date: 2026.02.26 HONEYWELL INTERNATIONAL INC

AI summary

A false alarm Artificial Intelligence (AI) Model is trained using metadata associated with alarms classified as false alarms and a true alarm Artificial Intelligence (AI) Model is trained using metadata associated with alarms classified as true alarms. An incoming alarm is received. The false alarm AI Model and the true alarm AI Model are both applied to the incoming alarm and both models classify the incoming alarm as either a false alarm classification or a true alarm classification. When the false alarm AI model and the true alarm AI Model agree, the incoming alarm is automatically classified accordingly. When the models do not agree, the incoming alarm is presented to an operator console of the BMS, and a manual classification of the incoming alarm is received from the operator console.