Electromagnetic Signal Classification Using Deep Learning for Pipeline Structural Health Monitoring

Pipeline networks are the cornerstone of global energy transportation, yet their integrity is constantly challenged by environmentalfactors, material degradation, and external threats, leading to costly failures and severe environmental and safety hazards. Traditional structuralhealth monitoring (SHM) methods often suffer from limitations such as delayed detection, high false alarm rates, and poor fault localization.This paper introduces a novel, non-invasive approach for pipeline anomaly detection and classification leveraging radio frequency (RF) sensingand deep learning. We propose a one-dimensional Convolutional Neural Network (1D CNN) framework that analyzes simulated reflectioncoefficient (S11) signatures from patch antennas, designed to detect and classify four distinct pipeline conditions: normal, crack, corrosion, andleak. A realistic dataset of electromagnetic responses, including varying levels of Gaussian noise to simulate real-world distortions, wasgenerated for model training and validation. The proposed 1D CNN achieved a perfect 100% classification accuracy on clean data andmaintained a robust 94% accuracy even under severe noise conditions, demonstrating excellent generalization capabilities. Notably, criticalfault conditions like leaks and normal states remained perfectly classified despite significant signal degradation. This work highlights thetransformative potential of integrating physics-grounded electromagnetic sensing with advanced deep learning algorithms, offering acomputationally efficient, scalable, and reliable solution for next-generation, intelligent pipeline monitoring systems, thereby enhancinginfrastructure safety and resilience.