Work

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

Year: 2025

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2025.3597079

Authors: Michele Quercio, Luca Santoro, Raffaella Sesana, Francesco Riganti Fulginei

Institutions: Roma Tre University, Politecnico di Torino

Language: English

Topic: Non-Destructive Testing Techniques

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075