Evaluating the Use of Surface Wave Ultrasonics for Near-Surface Rolling Contact Fatigue Depth Characterization
Bibliographic record
Abstract
Abstract Identifying and accurately characterizing shallow-depth rolling contact fatigue (RCF) in railway systems remains challenging with existing measurement technologies in widespread use such as bulk wave ultrasonics, eddy current, and magnetic flux leakage. The ability to accurately estimate RCF depth is important, for example for determining the risk of the damage feature, and for optimizing metal removal for rail reprofiling operations such as grinding and milling. Recently, surface wave ultrasonic inspection has demonstrated good potential for shallow depth RCF damage characterization based on low attenuation, long detection distances, high sensitivity to surface defects, and low susceptibility to changes in surface conditions. This paper reports proof of concept testing to further evaluate the potential use of surface wave ultrasonic measurement for shallow depth RCF characterization. A prototype benchtop system developed by Peak to Peak Measurement Solutions is used to conduct fundamental tests with a range of transducer frequencies and geometric arrangements on a steel test block with machined features intended to represent surface damage at suitable depths. The basic feasibility, sensitivity, and correlation of measured signals is reported and analyzed to assess the technology at a proof of concept level, setting the stage for future testing and practical evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".