Refrigerant leak detection using a sensor-reading context analysis
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Solution Overview
Problem
Conventional refrigerant leak detection systems using threshold-based sensors generate high rates of false alarms, leading to system downtime and unnecessary technician visits, particularly when using moderate-to-low global warming potential (GWP) refrigerants like A2L refrigerants, which are mildly flammable.
Innovation Solution
A sensor-reading context analysis system utilizing a sensor network and a controller with machine learning algorithms to analyze refrigerant and ambient condition parameters, reducing false alarms by considering operating conditions and patterns over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If threshold-based detection schemes are used, then the detection system is simple and easy to operate, but false alarm rates are high
Solution Approach 1:
The system transitions from static threshold-based detection to dynamic pattern recognition. The controller analyzes temporal patterns and sequences of sensor readings rather than comparing single readings against fixed thresholds, allowing the detection criteria to adapt dynamically to normal system variations while maintaining simplicity in hardware configuration.
Solution Approach 2:
Instead of discrete threshold comparisons, the system continuously monitors and analyzes sequences of sensor readings over time. This continuous analysis of reading patterns and trends enables reliable detection while filtering out transient false alarm conditions, maintaining both reliability and operational simplicity.
2Reliability
If frequent alarm checks are performed, then leak detection reliability is improved, but system downtime increases due to false alarms
Solution Approach 1:
The system performs preliminary analysis of sensor reading patterns before triggering alarms. By evaluating sequences of readings and identifying characteristic patterns of actual leaks versus normal variations, the system prepares and validates alarm conditions in advance, reducing unnecessary technician visits and system downtime while maintaining high detection reliability.
3Ease of manufacture
If simple threshold comparison is used, then the detection method is easy to implement, but trustworthiness of detection is compromised
Solution Approach 1:
The controller acts as an intermediary between simple sensor readings and alarm output. It introduces pattern recognition analysis as an intermediate processing step that evaluates sequences of readings against learned patterns, maintaining ease of implementation with standard controllers while significantly improving detection trustworthiness through intelligent analysis of reading patterns and temporal characteristics.
Data Source
AI summary
A detection assembly operable to detect a refrigerant leak event includes a sensor network and a controller. The sensor network is operable to generate sensor outputs including triggering-sensor (TS) outputs and triggering-sensor context (TSC) outputs. The controller is operable to perform a sensor-reading context analysis on the sensor outputs. The sensor-reading context analysis includes accessing a set of the sensor outputs that occurred within a context time window, along with determining that a pattern of the set of sensor outputs represents the refrigerant leak event.