Thermal Imaging for Low-Gravity Fire and Leak Detection
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Solution Overview
Problem
Detection of safety events such as pre-ignition combustion and leaks is challenging in low-gravity environments due to the undetectability of small smoke particles and invisible fires, leading to delayed detection that can be dangerous or deadly.
Innovation Solution
Utilizing thermal image sensors and machine learning models to process thermal images for detecting safety events, including fires and leaks, by classifying infrared radiation patterns and communicating alerts to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional smoke detectors are used in low-gravity environments, then the detection system is simple and inexpensive, but detection reliability deteriorates because small smoke particles from materials like Kapton and Teflon are undetectable
Solution Approach 1:
The patent replaces traditional mechanical smoke detection with thermal imaging technology that detects infrared radiation patterns. This substitution enables detection of invisible pre-ignition combustion and cool flames that produce no visible smoke, thereby improving detection reliability in low-gravity environments where conventional smoke detectors fail.
Solution Approach 2:
The system changes the detection parameter from visible smoke particle concentration to thermal infrared radiation patterns. By detecting temperature variations and thermal signatures rather than relying on smoke density, the system can detect safety events before they produce visible smoke, addressing the reliability issue in low-gravity environments.
2Reliability
If thermal image sensors with machine learning processing are implemented, then detection reliability improves for invisible fires, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary thermal imaging and machine learning analysis continuously to detect pre-ignition combustion patterns before they develop into visible fires. This preliminary detection action enables early warning of safety events that would otherwise remain undetected until they become critical.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the thermal image sensor and the detection decision. The AI model processes complex thermal patterns and identifies safety events automatically, reducing the need for complex hardware while improving detection reliability through intelligent pattern recognition.
3Loss of time
If detection systems rely on smoke particles being pulled through ventilation, then the system design is simple, but detection time increases causing dangerous delays
Solution Approach 1:
The thermal imaging system provides self-service detection by directly measuring temperature and infrared radiation patterns in the environment, independent of ventilation system operation. This eliminates the dependency on ventilation to bring smoke particles to the detector, enabling immediate detection of thermal anomalies without waiting for smoke to be pulled through the ventilation system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the detection of safety events in low-gravity environments by providing timely alerts and potentially initiating fire suppression systems, thereby reducing the risk of fire spread and ensuring astronaut safety.
Implementation Method 1
a thermal image sensor disposed within a low-gravity environment. A thermal image sensor, in some embodiments, is configured to take a thermal image of infrared radiation within a field of view of the thermal image sensor
Data Source
AI summary
Apparatuses, systems, methods, and computer program products for low-gravity safety event detection are described. A method includes receiving a thermal image of infrared radiation within a field of view of a thermal image sensor. A method includes processing a thermal image using a machine learning model to determine whether a safety event has occurred within a field of view. A method includes, in response to determining a safety event has occurred within a field of view, communicating to a user, on an electronic display screen of a hardware computing device, that the safety event has occurred.