Flame Detection FOV Misalignment Diagnostics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing flame detection systems lack information about the field of view (FOV) and fail to accurately identify critical regions, leading to misalignment and obstacle issues, which compromise the accuracy of flame detection and installation.
Innovation Solution
A system comprising sensors, imaging devices, and processors that capture real-time images, compare them with reference images, and determine misalignment and obstacle information within the FOV by masking IR channels with criticality levels and identifying key points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single pixel non-imaging detector is used for flame detection, then the system can detect flames from all directions within FOV, but the system cannot provide information about what regions are being viewed or seen
Solution Approach 1:
The patent divides the FOV into multiple zones with different criticality levels and uses multiple imaging devices positioned at different locations to capture images of different regions. This segmentation allows the system to track which specific regions are being viewed by the sensor while maintaining comprehensive detection coverage.
Solution Approach 2:
The patent introduces imaging devices as intermediary components that capture visual information about the FOV regions. These imaging devices act as mediators between the non-imaging flame detector and the environment, providing information about what regions are being viewed without interfering with the flame detection function.
2Reliability
If comprehensive FOV monitoring is implemented using multiple imaging devices, then misalignment and obstacle information can be detected, but the system complexity increases
Solution Approach 1:
The imaging devices serve multiple functions: they capture images for misalignment detection, identify obstacles in the FOV, and provide information about critical regions. This multi-functionality reduces the need for separate dedicated components for each function, thereby managing system complexity while improving reliability.
Solution Approach 2:
The system uses the existing imaging devices and their captured images to self-diagnose misalignment and obstacle conditions. The processors automatically compare images with reference images and generate diagnostics without requiring external intervention, reducing the complexity of manual monitoring and adjustment.
3Measurement precision
If real-time image comparison with reference images is performed, then misalignment information can be determined, but processing time and computational resources increase
Solution Approach 1:
The patent applies zone-based masking where only specific regions of interest with different criticality levels are processed in detail. By focusing computational resources on critical zones rather than the entire FOV uniformly, the system maintains high measurement precision for misalignment detection while reducing overall processing time.
Solution Approach 2:
The system performs partial image comparison by focusing on key features and critical zones rather than processing every pixel in the entire image. This partial action approach provides sufficient misalignment detection precision while significantly reducing computational burden and processing time.
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
Enables precise and early identification of flames, ensures accurate installation, and provides real-time monitoring for improved fire safety by addressing misalignment and obstacle detection.
Implementation Method 1
The one or more sensors are one or more infrared (IR) sensors, flame sensors, or photodiodes
Data Source
AI summary
A flame detection system is disclosed. The flame detection system comprises one or more sensors to detect one or more targets within a field of view (FOV). Further, at least one imaging device is installed based on a distance between one or more sensors, one or more targets, and the FOV. The at least one imaging device is configured to capture one or more real time images of one or more targets within the FOV. Further, one or more processors are communicatively coupled to one or more sensors and at least one imaging device. The one or more processors are configured to receive one or more real time images, compare one or more real time images with at least one reference image and determine a misalignment information, obstacle information, or misalignment and obstacle information within the FOV based at least on comparison.