Electromagnetic Signal Classification Using Deep Learning for Pipeline Structural Health Monitoring

A 1D convolutional neural network that reads radio-frequency antenna reflection signatures (S11) to classify pipeline conditions — normal, crack, corrosion, or leak — without any physical contact. It reached 100% accuracy on clean signals and held 94% under heavy simulated noise, with leaks staying perfectly classified even under degradation.