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

Advanced Feature Analysis of Eddy Current Testing Signals for Rail Surface Defect Characterization / Quercio, M.; Santoro, L.; Sesana, R.; Riganti Fulginei, F.. - In: IEEE ACCESS. - ISSN 2169-3536. - 13:(2025), pp. 1-15. [10.1109/ACCESS.2025.3597079]

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

Quercio M.;Santoro L.;Sesana R.;
2025

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
2025
File in questo prodotto:
File Dimensione Formato  
Advanced_Feature_Analysis_of_Eddy_Current_Testing_Signals_for_Rail_Surface_Defect_Characterization.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 5.6 MB
Formato Adobe PDF
5.6 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3002376