Multi-Modal Imaging for Automated Ethane Leak Detection
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Solution Overview
Problem
Current leak detection methods for ethane in petrochemical industries are labor-intensive, costly, and prone to false alarms, with existing optical gas imaging (OGI) and acoustic sensors requiring manual intervention, while IR imaging lacks semantic information for precise detection.
Innovation Solution
A multi-modal imaging system combining visible (VI) and infrared (IR) cameras for ethane leak detection, utilizing information fusion techniques to enhance semantic knowledge and improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual OGI scanning is used for leak detection, then detection capability is achieved, but labor intensity and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical scanning with automated optical imaging systems. Multiple cameras (visible light, infrared, thermal) are positioned to capture images of the pipeline area, eliminating the need for manual surveyors to physically scan each sector while maintaining detection capability.
Solution Approach 2:
The system creates multiple visual copies of the monitoring area using different imaging modalities (visible light images, infrared images, thermal images). These image copies are processed through background subtraction and fusion algorithms to detect leaks automatically, replacing manual inspection with automated image analysis.
2Measurement precision
If experienced surveyors are employed for leak detection, then detection accuracy improves, but training and employment costs increase
Solution Approach 1:
The system enables self-service leak detection through automated image processing algorithms. The background subtraction module automatically compares current images with historical background images, and the fusion module automatically integrates multi-modal images, eliminating the need for human surveyors to perform manual analysis while maintaining high detection accuracy.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously refined. The background subtraction process uses historical data to improve future detections, and the fusion module iteratively combines information from multiple sources to enhance detection accuracy, replacing the need for experienced human judgment with automated feedback-driven analysis.
3Reliability
If acoustic sensors are deployed for continuous monitoring, then leak detection capability is achieved, but the number of sensors required increases due to signal attenuation
Solution Approach 1:
The patent merges multiple imaging modalities (visible light cameras, infrared cameras, thermal cameras) into a unified monitoring system. By fusing images from different modalities, the system achieves comprehensive coverage and continuous monitoring capability with fewer sensor units compared to deploying multiple acoustic sensors to compensate for signal attenuation.
4Extent of automation
If single-modal IR imaging is used for leak detection, then automated detection is achieved, but semantic information is insufficient for precise detection
Solution Approach 1:
The system uses composite information from multiple imaging modalities. Visible light images provide semantic context and object identification, infrared images provide thermal signatures of leaks, and thermal images provide temperature distribution. The fusion module combines these complementary information sources to achieve both automation and precise detection with sufficient semantic information.
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 continuous, automated leak detection with reduced labor costs and improved sensitivity, leveraging the rich semantic information from VI and IR modalities to distinguish ethane leaks accurately.
Implementation Method 1
Optical gas imaging (OGI) is a prevalent technique that employs middle wavelength infrared (IR) cameras to perceive ethane leaks from pipelines
Data Source
AI summary
A leak of a cold fluid (e.g., a chilled fluid, or a fluid that is initially pressurized and becomes cold on leakage) is detected using a sequence of Infrared (IR) and Visual (VI) images. Using neural nets, in each of VI and IR image-level features are extracted from images and compared with image-level features from images of different times to obtain motion-enhanced features. The motion enhanced features from VI and IR are then compared to obtain fused features from which the leak is detected. The image-level features may be extracted using a neural net with multiple stages. The motion-enhanced and fused features may be obtained in parallel using the image-level features from the multiple stages, and the leak detection based on the stage-specific fused features.