Gaussian-Pulsed Eddy Current Testing With Spiking CNN for Spectral Feature-Based Corrosion Detection

Corrosion detection in critical structural components is a persistent challenge in the aerospace and structural health monitoring industries. This work introduces an advanced Gaussian-pulsed eddy current testing (GPECT) system integrated with a spiking neural network (SNN) for precise and efficient detection of corrosion. Unlike conventional square-pulse excitation, the proposed GPECT system employs a Gaussian pulse, enabling the selection of the frequency bandwidth to enhance defect detection sensitivity. The system’s sensor probe features a coil for magnetic excitation and a Hall sensor centrally positioned to capture the resulting magnetic field signals. These signals are transformed into the frequency domain using a short-time Fourier transform (STFT), facilitating the extraction of key spectral features indicative of corrosion. Leveraging the temporal and energy-efficient processing capabilities of the SNN, which incorporates spike generation, convolutional feature extraction, and robust classification, the system achieves significant improvements in detectability and energy savings (ten times smaller than the conventional NN model). Experimental evaluations on aluminum specimens with artificially induced corrosion of varying depths and diameters validate the system’s effectiveness, demonstrating high accuracy and robustness in differentiating corroded and noncorroded regions. This work establishes a robust foundation for next-generation nondestructive testing techniques, pushing the frontiers of corrosion detection in high-stakes applications.