Wearable Thermal Sensor Flashover Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fire emergency systems lack the ability to predict flashover events effectively, which can lead to dangerous situations for first responders due to the inability to detect the auto-ignition of combustible gases and subsequent explosions, and current solutions add complexity and cost without providing a reliable prediction method.
Innovation Solution
A wearable device equipped with thermal imaging cameras or infrared sensors that use machine learning models to analyze thermal data, predicting the risk of flashover events by determining the time-varying thermal profile and triggering alerts based on the risk of ignition, allowing for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gas sensors are used to detect combustible gases for flashover prediction, then the ability to predict flashover is improved, but the cost and device complexity increase
Solution Approach 1:
The patent extracts the essential predictive information (thermal environment characteristics) from the complex combustion process, focusing only on the thermal data needed to predict flashover rather than detecting all combustion byproducts. This simplifies the system by removing the need for multiple gas sensors while maintaining prediction capability through thermal imaging and machine learning analysis of temperature distributions and thermal gradients.
2Ease of operation
If thermocouples are mounted on first responder gear for ambient temperature measurement, then the measurement is portable, but the measurement accuracy deteriorates due to movement patterns
Solution Approach 1:
The patent introduces thermal imaging cameras as an intermediary device that measures temperature remotely without being affected by first responder movement. The thermal camera captures thermal radiation from the environment, allowing accurate ambient temperature measurement while the first responder moves freely, eliminating the coupling between movement and measurement error.
3Ease of operation
If thermal imaging cameras are used for non-contact temperature measurement, then the portability and ease of deployment are improved, but the ability to predict flashover events deteriorates compared to existing systems
Solution Approach 1:
The patent applies preliminary action by using machine learning models trained on fire dynamics data to analyze thermal patterns and predict flashover before it occurs. The system processes thermal imaging data to identify precursor indicators of flashover, such as specific temperature distributions and thermal gradient patterns, enabling early warning and prediction rather than just detection.
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
The system provides an efficient method for predicting flashover events, enabling first responders to take necessary precautions and reducing the risk of serious burns or death by alerting them to the current and future risk levels of flashover, thus enhancing their safety during fire rescue operations.
Implementation Method 1
Non-contact measurements may be performed using infrared (IR) radiation, either by performing spot measurements or through a thermal imaging camera (TIC)
Implementation Method 2
one or more infrared (IR) spot sensors are implemented to generate thermal data of the environment around a user wearing the IR spot sensors
Data Source
AI summary
A system, wearable device and management device provided for predicting a flashover event. According to one aspect of the disclosure, a wearable device for predicting a flashover event is provided. The wearable device includes at least one thermal sensor configured to generate thermal data associated with an environment, and processing circuitry configured to: determine a risk of ignition of at least one combustible gas in the environment based on the thermal data, and trigger at least one action based on the determined risk of ignition.