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Abstract
The occurrence of surface and sub-surface local damage, such as squats and studs, primarily attributed to rail-wheel contact within rail tracks, underscores the necessity for developing non-destructive testing methods. Such methods should ideally be high-speed, non-contact, and readily adaptable. Initial studies were undertaken to ascertain the optimal configurations of directional transmit and differential receiver eddy-current testing (ECT) sensors for detecting artificially induced rail defects. Discrepancies were observed in the response of the developed ECT sensors when compared to real rail damage. Consequently, this research evaluates the real-time efficacy of the original figure-8 transmit configuration with non-unity aspect ratio differential receivers against a novel dual figure-8 transmit design paired with unity aspect ratio receivers. The comparison focuses on their respective capabilities in detecting actual defects induced within rail steel samples, aiming to provide crucial insights for future in-line measurements using a trolley on a live rail network. ECT achieved a signal-to-noise ratio (SNR) exceeding 47 for rail studs, while μ-CT scans validated these results by confirming an approximate defect depth of 7.1 mm and width of 33.54 mm. Quantitative characterization was achieved through an evaluation of the SNR, indicating the desirability of lower frequencies for facilitating electronic data acquisition and processing. Additionally, lift-off sensitivity analysis demonstrated that the optimized probe design, operating at the optimum frequency, could detect targeted defects with up to a 10 mm lift-off distance, achieving an SNR of 5.
| Original language | English |
|---|---|
| Article number | 119611 |
| Number of pages | 14 |
| Journal | Measurement |
| Volume | 259 |
| Issue number | A |
| Early online date | 7 Nov 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 7 Nov 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Authors
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Dive into the research topics of 'Design and real-time sensitivity evaluation of directional eddy current probes on extracted rails'. Together they form a unique fingerprint.Projects
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Development and enhancement of systems based on directional eddy current probes for railway defect detection using machine learning methods
Mussatayev, M. (Principal Investigator)
1/01/25 → 31/12/27
Project: Research