Weighted Spectral Flame Detection for False Source Discrimination
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Solution Overview
Problem
Existing flame detection systems struggle to differentiate between actual flames and false flame sources such as arc welding, halogen lights, or heaters, leading to false alarms.
Innovation Solution
A flame detection system utilizing a combination of image capturing units and flame detectors that capture multiple spectral band data, including infrared and ultraviolet bands, with a processor to classify images and assign weights to spectral band data, determining the presence of flames by comparing weighted counts to threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing flame detection systems use simple detection methods, then the device complexity is low, but the reliability of flame detection deteriorates due to false alarms from friendly flames and false flame sources
Solution Approach 1:
The detection system is segmented into multiple independent detection channels (UV channel, IR channel, visible light channel) that operate in parallel. Each channel detects different spectral characteristics of flames, and the final determination is made by综合分析 the results from all channels. This segmentation allows the system to maintain high reliability while keeping each individual detection module relatively simple.
Solution Approach 2:
The detection system uses a single imaging device that can detect multiple types of radiation (UV, IR, and visible light) by incorporating different spectral filters. This multi-functional approach allows one device to perform what would otherwise require multiple separate detectors, reducing overall system complexity while improving reliability through multi-spectral analysis.
2Measurement precision
If the system captures multiple spectral band data to improve detection accuracy, then the reliability improves, but the device complexity and data processing complexity increase
Solution Approach 1:
The system dynamically adjusts the detection process by first capturing a preliminary image to analyze the field of view for presence of friendly flames or false flame sources. Based on this dynamic assessment, the system then decides whether to proceed with full multi-spectral flame detection or to suppress alerts. This dynamic approach optimizes measurement precision while avoiding unnecessary complex processing in situations where false alarms are likely.
3Reliability
If the system suppresses alerts in presence of friendly flames or false flame sources, then the reliability improves by reducing false alarms, but the response time to actual flames may be delayed
Solution Approach 1:
The system performs preliminary analysis of the field of view before making final flame detection determinations. By first identifying the presence of friendly flames or false flame sources in the scene, the system can pre-adjust its detection sensitivity and alert suppression strategy. This preliminary action allows the system to maintain fast response to actual flames while reducing false alarms, as the preliminary analysis is performed continuously in the background without waiting for separate detection events.
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
Effectively distinguishes between real flames and false sources, reducing false alarms by accurately identifying flames through a weighted count analysis.
Implementation Method 1
at least one image capturing unit to capture one or more images of a field of view (FOV)
Implementation Method 2
at least one flame detector configured to capture a first set of spectral band data and a second set of spectral band data present in the FOV
Implementation Method 3
the at least one flame detector corresponds to a combination of an infrared (IR) sensor and an ultraviolet (UV) sensor
Data Source
AI summary
A flame detection system comprises an image capturing unit configured to capture images of a field of view (FOV), a flame detector configured to capture a first set of spectral band data and a second set of spectral band data, and a processor configured to classify the one or more images and first set of spectral band data, assign a weight to each second set of spectral band data, receive a flame count for each second set of spectral band data, determine a weighted count for each flame count, determine a quantity of weighted counts exceeding its respective threshold value, and determine that a flame exists within the FOV when the quantity is greater than or equal to a quantity threshold value, and determine that a flame does not exist within the FOV when the quantity is less than the quantity threshold value.