Evaluation of typical rail defects by induction thermography: experimental results and procedure for data analysis during high-speed laboratory testing
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".