Evaluation of typical rail defects by induction thermography: experimental results and procedure for data analysis during high-speed laboratory testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rail inspection via non-destructive testing (NDT) techniques is a critical area of research in the railway industry, significantly affecting transport safety and security. Conventional NDT methods face limitations in on-site applications, with emerging techniques improving defect detectability and inspection speed. Recent advances have highlighted infrared thermography, particularly induction thermography, as a promising alternative due to its non-contact, full-field capabilities for detecting both surface and subsurface rail defects. This study explores induction thermography in detecting key defects such as transversal cracks and head checks in different rail tracks. Novel approaches and procedure for data reconstruction that enhance the thermographic inspection results and allow for dynamic testing conditions are proposed. Additionally, the potential for high-speed on-site applications was investigated, utilizing infrared mirrors and optimally shaped coils. Various test parameters, including geometrical resolution, excitation power, and inspection speed up to 20 km/h, were systematically examined.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it