Evaluation of ride quality and rail surface roughness from vibration analysis of in-service passenger rail cars
Bibliographic record
Abstract
Regulatory and special visual inspection of railway tracks is important to ensure the safe and secure operation of trains. Due to increased traffic volumes, operational speeds and axle loads, new technologies are emerging to collect and monitor data more frequently and in a shorter schedule-window. This paper presents a feasibility study to evaluate ride quality and rail surface roughness from car body and axle box accelerations mounted on service passenger rail cars. Accelerometers are mounted under car body of a rail car and on two axle boxes to evaluate ride quality and rail surface roughness, respectively. The repeatability of measurements, and impact of track features such as bridges, grade crossing and switches on the ride quality is studied by applying a weighted filtering method as per ISO 2631-1997 standard. The ability of axlebox acceleration signals to quantify the rail surface roughness are evaluated by comparing the calculated rail displacements with rail surface profile measurements recorded by a track geometry inspection car. Rail surface relative displacements are calculated from double integration of axle-box accelerations. The results of vibration analysis show that there is a meaningful correlation between increased magnitude of ride quality index and the location of some track features. The comparison between the calculated rail displacements and measured rail surface profile along the entire length of the studied track confirmed that the axle-box acceleration and the applied analytical technique are appropriate for evaluation of rail surface roughness.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".