AI Alarm Cross-Verification System for Maintenance
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Solution Overview
Problem
Current alarm systems face challenges in accurately classifying alarm types during predictive and preventive maintenance, leading to misclassification and increased costs due to false or mismatched alarms, which can result in delays and inefficiencies, especially for critical issues.
Innovation Solution
A method and system utilizing AI and ML techniques to cross-verify alarms in real-time by identifying primary and secondary variables, labeling them based on predictive models, and correlating labels to accurately classify alarms and recommend maintenance, displayed on a dashboard for efficient technician allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-based cross-verification system is implemented, then alarm classification accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the alarm verification process into separate functional modules: a primary engine for initial alarm detection, a secondary engine for cross-verification, and a validation engine for final confirmation. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary validation engine that sits between the primary alarm generation system and the final alarm dispatch system. This intermediary layer performs cross-verification by comparing alarms from multiple sensors and sources, acting as a mediator that filters out false alarms while maintaining system complexity at acceptable levels.
2Measurement precision
If manual cross-verification of alarms is performed, then alarm accuracy is improved, but time consumption increases
Solution Approach 1:
The system implements self-service verification where the alarm system automatically cross-verifies its own alarms using multiple sensors and historical data patterns. The AI models continuously learn from past alarm resolutions and automatically adjust verification criteria, eliminating the need for manual intervention while maintaining high accuracy.
Solution Approach 2:
The validation engine incorporates feedback mechanisms that continuously monitor alarm resolution outcomes and feed this information back into the AI models. This feedback loop allows the system to learn from actual alarm resolutions and improve its verification accuracy over time, reducing both time consumption and error rates.
3Speed
If all alarms are attended immediately, then response speed is improved, but resource waste increases
Solution Approach 1:
The system applies different quality levels of verification to different alarm types. Critical alarms undergo full cross-verification with multiple sensors and historical data, while less critical alarms receive streamlined verification. This local differentiation ensures that resources are not wasted on low-priority alarms while maintaining rapid response for critical issues.
Solution Approach 2:
The validation engine dynamically changes verification parameters based on alarm severity, historical data patterns, and current system state. By adjusting the depth and type of verification based on these parameters, the system achieves fast response for high-priority alarms while reducing verification intensity for lower-priority alarms, thereby minimizing resource waste.
Data Source
AI summary
A method and system for cross-verification of alarms in real-time comprising identifying primary variables and secondary variables causing the event, labelling the primary variables and the secondary variables by primary engine and secondary engine based on Artificial Intelligence based predictive model building, predicting the labels by one or more inference engine based on previous history and data patterns, triggering secondary engine for cross-verification of alarms whenever there is a prediction from the primary engine, identifying the correlation between the labels from the primary engine and the secondary engine by validation engine, identifying alarm type based on correlation, recommending predictive maintenance and displaying on dashboard the cross-verification status of the alarms. The method reduces misclassification of alarm types based on predictive or preventive maintenance, reduces the maintenance costs of assets, and helps in prioritizing the critical alarms based on the alert type.