Visual fingerprinting accurately identifies and analyzes atmospheric corrosion in copper tubes, enhancing predictive maintenance.
Using a machine learning approach, the model achieves over 85% accuracy in various environmental conditions, proving its robustness.
The analysis leverages explainable artificial intelligence, which provides insights into the decision-making process by showing the reasoning behind predictions.
Understanding corrosion patterns can help in implementing preventive measures, potentially saving costs and improving safety in engineering applications.