Fuel Assembly Debris Detection Using CNN Video Inspection
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Solution Overview
Problem
Current television inspection methods for detecting migrating bodies in fuel assemblies of nuclear power plants are prone to human fatigue and vigilance issues, leading to missed detections, which can compromise the stability and safety of the assemblies during reloading.
Innovation Solution
An image recognition algorithm using a convolutional neural network is employed to assist operators in detecting migrating bodies on the anti-debris grid of fuel assemblies, providing real-time alerts and generating inspection reports, and allowing for iterative learning from operator feedback to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual television inspection methods are used to detect migrating bodies, then operators can visually examine the anti-debris grid, but human fatigue and vigilance issues lead to missed detections and reduced reliability
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated image recognition algorithm that processes video frames from television inspections. The algorithm automatically detects migrating bodies on the anti-debris grid, eliminating human fatigue and vigilance issues while maintaining continuous monitoring capability.
Solution Approach 2:
The patent introduces an intermediary image recognition algorithm that acts as a bridge between the television inspection system and the final detection output. This intermediary process enhances the inspection system by automatically identifying migrating bodies that might be missed by human operators.
2Measurement precision
If manual inspection of video streams is performed, then operators can identify migrating bodies, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces time-consuming manual video analysis with an automated image recognition algorithm that rapidly processes video frames. The algorithm achieves higher detection accuracy by consistently identifying migrating bodies across all frames without the variability and time constraints of manual inspection.
Solution Approach 2:
The image recognition algorithm performs self-service by automatically analyzing video frames, detecting migrating bodies, and generating results without requiring continuous human intervention. The system independently completes the inspection task, significantly reducing analysis time while maintaining or improving detection accuracy.
3Reliability
If traditional inspection methods are used, then the process is simple and straightforward, but migrating bodies may be missed compromising assembly stability
Solution Approach 1:
The patent replaces simple but unreliable manual inspection with a more complex automated image recognition system. This substitution prioritizes assembly stability by ensuring no migrating bodies are missed, accepting the increased system complexity as necessary for achieving the critical reliability goal.
Solution Approach 2:
The patent implements feedback mechanisms where the image recognition algorithm continuously processes inspection data and provides results that can be verified and refined. This feedback loop enhances detection reliability, ensuring that migrating bodies are identified to maintain assembly stability during reactor operation.
Data Source
AI summary
The invention relates to a process for aiding the detection of migrating bodies within a fuel assembly of a nuclear power plant and more particularly on the anti-debris grid of the lower end piece of said assembly, during which at least one camera is controlled in the direction of said assembly and the stream of images recorded by said at least one camera is directed towards a man-machine interface which comprises at least one screen allowing a first operator to view said stream of video images, characterized in that it comprises a first step of detecting, using an image recognition algorithm, said migrating bodies, as well as at least a second step of alerting said operator if said algorithm has detected the potential presence of at least one migrating body.