Rail grinding on the South Central Railway Region of Indian Railways
Bibliographic record
Abstract
Indian Railways (IR) has implemented a preventive-gradual rail grinding program in 2011 on the South Central Railway (SCR) and North Central Railway (NCR) to improve passenger safety, increase railway capacity and reduce track maintenance costs, rail failures and speed restrictions. The National Research Council Canada, Surface Transportation (NRC-ST) was commissioned in 2009 by IR to train engineers in the gathering and assessment of data for the development of new wheel and rail profiles. NRC-ST used these data to engineer a wheel profile and four rail profiles. The design included two tangent profiles to promote uniform wheel tread wear. NRC-ST recommended a lubrication program to reduce high rail gauge face wear. NRC-ST conducted a school for IR engineers in July 2010 to train them in wheel/rail interaction and best practices in rail grinding and friction management. Field training was conducted in India for best practices in rail grinding implementation during the start up of the high-production rail grinders in early 2011. NRC-ST developed a grinding program based on the route-specific curvature and annual tonnage. The program began with accelerated preventive-gradual grinding at 25 million gross tonne (mgt) intervals for the first two cycles then changed to preventive grinding at 50 mgt intervals. The program detailed the target profile, the pattern, the grinding speed and the number of passes required for each curve and tangent segment. The laser profile measurement system and proprietary software on board the grinder measures the rail profile and selects the second and third pass grinding patterns for curves. Program results after the first year indicate that the rail grinding program has reduced wheel/rail impacts, rail/weld fatigue defects and rail and weld failures. Contact stresses have also reduced on areas of the rail where there was rolling contact fatigue (RCF). IR anticipates the grinding program will increase wheel and rail life and reduce maintenance costs for track components. Energy costs should also reduce due to the smoother rail surface, the elimination of hunting and improved curving.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".