Predicting Sensor False Alarms via Statistical Inference Models
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Solution Overview
Problem
Conventional security systems degrade over time, leading to potential threats and issues with false alarms, which can be costly and disruptive, and there is a need for improved methods to monitor and predict system reliability and false alarm occurrences.
Innovation Solution
A security system that utilizes cloud-connected sensors to collect and analyze data, employing predictive algorithms and statistical models to forecast sensor failures and false alarms, allowing for targeted maintenance and reducing downtime and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security systems operate over time, then they provide continuous protection, but system reliability degrades and false alarms increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting sensor data and updating degradation models to predict future failures before they occur. The statistical inference models analyze historical data patterns to forecast when sensors are likely to fail or generate false alarms, enabling maintenance to be scheduled in advance rather than waiting for actual failures.
Solution Approach 2:
The system implements feedback loops where sensor data is continuously collected, analyzed against degradation models, and used to update predictions of system reliability. The results feed back into maintenance scheduling decisions, creating a closed-loop system that adapts to actual system behavior over time and improves prediction accuracy as more data is accumulated.
2Reliability
If sensors are deployed to detect threats, then security coverage is improved, but false alarm occurrences increase
Solution Approach 1:
The system changes parameters by dynamically adjusting sensor sensitivity thresholds and alarm trigger criteria based on learned environmental patterns and historical data. The statistical models identify normal variation ranges for each sensor type and adjust detection parameters to distinguish between legitimate threats and normal environmental fluctuations, thereby reducing false alarms while maintaining detection accuracy.
3Loss of information
If continuous monitoring is implemented, then system health is tracked, but data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features and parameters from the continuous sensor data stream for analysis, rather than processing all raw data. The degradation models focus on specific indicators of sensor health and performance degradation, filtering out redundant information and concentrating computational resources on the most predictive variables for failure detection.
Data Source
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AI summary
A diagnostics and prediction system including a cloud system that continuously collects operating parameters from each of a number of environmental sensors and provides access to this data by a plurality of processing applications including (1) a predictive modeling system including (a) a health prediction system, (b) a sensor false alarm prediction system, (c) a zone false alarm prediction system and (d) a reporting system, (2) a system that diagnoses and predicts environmental hazardous areas and clusters areas based upon concentrations of CO in the site or building; and (3) a battery prediction system that predicts a battery life for the sensor.