AI Anomaly Detection for Nuclear Reactor Brick Arrays
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Solution Overview
Problem
Current methods for inspecting nuclear reactor components are labor-intensive, risky for human operators, and inefficient, with manual data interpretation and lack of predictive capabilities for future defect development.
Innovation Solution
A computer-implemented method using artificial intelligence to automatically detect defects in nuclear reactor components from collected picture data, and predict future system states based on identified defects, integrating classification models and neural network algorithms with physical modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used by human operators, then detailed visual assessment can be performed, but operator safety is compromised and inspection time increases
Solution Approach 1:
The patent uses Remote Visual Inspection (RVI) cameras to capture images of brick arrays, creating visual copies that can be analyzed without human operators physically present. The system processes these images through AI algorithms to detect anomalies, thereby maintaining inspection accuracy while eliminating operator exposure to hazardous radiation environments
Solution Approach 2:
The patent replaces manual mechanical inspection processes with an automated system combining RVI cameras, image processing algorithms, and AI anomaly detection. This substitution eliminates the need for human operators to physically access hazardous areas while maintaining or improving detection capabilities through computational analysis
2Reliability
If frequent inspections are conducted to prevent defects, then system reliability improves, but productivity decreases due to regular shutdowns
Solution Approach 1:
The patent implements AI-based anomaly detection that identifies potential defects in brick arrays before they develop into critical failures. By detecting early signs of degradation patterns, the system enables predictive maintenance scheduling that prevents catastrophic failures without requiring frequent preventive shutdowns, thus maintaining both safety and productivity
Solution Approach 2:
The system continuously monitors brick array conditions through RVI and AI analysis, providing feedback on degradation trends. This ongoing assessment allows operators to schedule maintenance based on actual condition rather than fixed intervals, optimizing the balance between reliability and productivity
3Measurement precision
If Finite Element Methods are used for defect prediction, then physical accuracy improves, but computational time increases significantly
Solution Approach 1:
The patent employs lightweight machine learning models trained on historical RVI data that can quickly assess current brick array conditions without requiring intensive computational resources. These models provide sufficient accuracy for operational decision-making while executing in real-time or near-real-time, unlike heavy FEM simulations
Solution Approach 2:
The system uses AI algorithms that analyze only the most relevant features from RVI images (such as crack patterns, displacement, and anomaly signatures) rather than performing complete physical simulations. This selective analysis provides adequate prediction accuracy for maintenance scheduling while significantly reducing computational burden
Data Source
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AI summary
The invention relates to a computer implemented method for monitoring a state of a system in a nuclear site, at least parts of the system being pictured by at least one camera, the method comprising: - collecting (S1) pictures of at least one part of the system, taken by at least one camera over time, - implementing (S2) a first artificial intelligence to detect at least one defect in said pictures, - implementing (S3) a second artificial intelligence to provide a prediction of a future state of said part of the system, from the at least one defect and according to a current operation mode of the system, - operating (S4) the system according to an adapted operational procedure, depending on said prediction.