Advanced Feature Analysis of Eddy Current Testing Signals for Rail Surface Defect Characterization

The maintenance of the railways is of paramount importance for safe and reliable transport. Eddy Current Testing (ECT) provides high-resolution time-series signals that capture subtle anomalies on the rail surface. This paper expands on previous analyses by combining classical time-frequency methods (short-time Fourier transform and continuous wavelet transform) and estimation of fractal dimensions with advanced feature extraction approaches, including wavelet sub-band decomposition, Hilbert–Huang transform, peak analysis and entropy metrics. Subsequently, a Random Forest classifier is applied to each set of characteristics, and we report comparative accuracy results on a dataset comprising rail segments with joints, welds, or squats. Experimental findings reveal that the Hilbert–Huang transform features yield the highest accuracy (93.28 %), while simpler features, such as peak counts, are less discriminative (46.93 %). These results underscore the effectiveness of using multiple signal-decomposition strategies and advanced analytics to robustly detect and categorize surface defects for better rail-maintenance decisions.