CML Corrosion Instruction Using AI State and Type Prediction

The patent replaces traditional mechanical/time-based simulation methods with artificial intelligence models (deep learning and machine learning models) that process visual images and measurement data to predict corrosion states. This substitution enables high-accuracy prediction while maintaining inspection efficiency, as the AI models can rapidly analyze multiple parameters and generate predictions without requiring lengthy physical simulations.

The patent transforms the corrosion prediction approach by changing from time-based dynamic simulation to a multi-parameter analysis method using AI models. The system considers visual state parameters, severity parameters, and environmental parameters simultaneously, allowing accurate prediction of corrosion development without relying on lengthy time-based simulations. This parameter transformation enables both high accuracy and efficiency.

The patent implements local quality by training separate AI models for each Condition Monitoring Location (CML) using location-specific visual images and measurement data. Each CML receives customized prediction based on its unique characteristics, material properties, and environmental conditions. This approach maintains simplicity in implementation while achieving high specificity, as the system automatically adapts to each location's unique features through data-driven model training.

The patent applies preliminary action by pre-training AI models with historical visual images and measurement data from each CML before actual corrosion prediction is needed. This preliminary training phase enables the models to learn location-specific corrosion patterns and behaviors, so that when predictions are required, they can be generated quickly with high specificity without requiring complex real-time analysis.

The patent implements dynamics by transitioning from fixed-schedule inspections to dynamic, condition-based inspection scheduling. The AI models continuously predict corrosion states and severity, enabling the system to adapt inspection frequencies based on actual corrosion development rates. When corrosion is slow or stable, inspections are spaced out; when acceleration is detected, inspection frequency increases automatically. This dynamic approach maintains high safety monitoring while optimizing resource usage.

The patent applies feedback by using AI model predictions of future corrosion states to inform subsequent inspection scheduling decisions. The system continuously monitors predicted corrosion severity and adjusts inspection timing based on this feedback loop. This enables safety-critical inspections to be prioritized while reducing or eliminating unnecessary inspections, thereby maintaining high reliability while improving inspection efficiency and resource utilization.