Virtual Maintenance Manager for False Alarm Prioritization
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Solution Overview
Problem
Building management systems (BMS) face challenges in efficiently prioritizing and managing high volumes of event and alarm data, particularly with frequent false alarms that can lead to resource burdens and significant costs, often due to hardware or software misconfiguration or misuse.
Innovation Solution
A user interface and method utilizing natural language processing to analyze and classify alarm events, determine if they are false, and generate recommendations for reducing false alarms, including work orders, behavioral, and configuration changes, facilitated by a virtual maintenance manager.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If BMS handles high volumes of event and alarm data, then monitoring coverage is improved, but processing complexity and resource burden increase
Solution Approach 1:
The alarm management system segments alarm data processing into multiple priority levels (critical, high, medium, low) and categories (false alarm, equipment failure, safety issue, maintenance reminder). This segmentation allows the BMS to handle high volumes of alarm data systematically by processing different types of alarms through specialized workflows, reducing overall processing complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent introduces an intermediary alarm management platform that sits between the BMS and end users. This intermediary layer automatically filters, prioritizes, and routes alarm data, preventing overwhelming volumes of raw alarm data from reaching operators. The intermediary applies machine learning models to predict false alarms and automatically suppresses low-priority notifications, thereby reducing processing complexity while preserving reliable monitoring.
2Measurement precision
If BMS processes all alarm events, then detection completeness is improved, but false alarm impact increases
Solution Approach 1:
The system implements feedback loops where alarm processing outcomes are continuously analyzed to improve future alarm filtering. Machine learning models learn from historical alarm data, operator responses, and false alarm patterns to refine detection accuracy. The system provides feedback to operators about alarm accuracy and adjusts detection thresholds dynamically, maintaining detection completeness while reducing false alarm impact through continuous improvement.
Solution Approach 2:
The patent dynamically changes detection parameters based on contextual information. Alarm thresholds, sensitivity levels, and notification priorities are adjusted in real-time based on equipment operating conditions, time of day, operator availability, and historical patterns. This parameter adaptation allows the system to maintain high detection completeness for genuine issues while automatically reducing sensitivity for conditions prone to false alarms.
3Loss of information
If BMS provides detailed alarm information, then information completeness is improved, but user interface complexity increases
Solution Approach 1:
The user interface dynamically adapts its complexity based on user role, experience level, and current operational context. First-time users see simplified views with only critical alarms and basic actions, while experienced operators can access detailed alarm analysis, historical data, and advanced filtering options. The interface dynamically reorganizes information presentation based on the type of alarm being viewed, providing detailed technical information for equipment failures while showing simplified safety protocols for emergency alarms.
Solution Approach 2:
The patent adds temporal and hierarchical dimensions to alarm information presentation. Instead of presenting all alarm details simultaneously in a flat structure, the system organizes information hierarchically (summary level → detailed level → technical level) and temporally (current status → historical context → predictive analysis). Users can navigate through these dimensions progressively, accessing detailed information only when needed, thereby maintaining information completeness while reducing perceived interface complexity.
Data Source
AI summary
A user interface for a security system includes a processing circuit, the processing circuit including a processor and memory coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive, from a user via a user device, a user request for information relating to the security system, determine, using natural language processing, an intent and one or more entities associated with the user request, the intent describing a purpose of the user request and the one or more entities describing a type or source of security system data, generate a response to the user request based on the intent and the one or more entities, wherein the response is a graphical display of security system data, and send the response to the user device.