Implementation of preventive rail grinding on Fortescue Metals Group 40 tonne axle load railway
Bibliographic record
Abstract
Fortescue Metals Group Limited (Fortescue) is the world’s fourth largest producer of iron ore, operating over 600 track kilometres (430 route kilometres) located in the Pilbara of Western Australia. Revenue service began in April 2008 running trains with 25.6 tonne axle loads and was gradually transitioned to 40 tonne axle loads by January 2010. Sroba Rail Services (SRS) was commissioned by Fortescue to review the rail grinding strategy on several occasions between October 2009 and July 2013. The reviews determined there was significant over-grinding of the rail to produce the two rail profiles used throughout the track (the SP and SP1 target profiles). The end result was narrow wheel/rail contact bands, requiring the rolling stock maintenance teams to carry out premature machining of wheels due to hollowing which could be partly attributed to running on one narrow contact band in tangent track. SRS also identified a lack of rail grinding capacity to maintain the rail profiles on mainline and switches. In July 2013, Fortescue commissioned SRS and the rail division of the National Research Council Canada (NRC) to engineer rail profiles specific to Fortescue’s operations. NRC designed five new target rail profiles. These profiles were implemented with a preventive-gradual grinding strategy using two new high production grinders, one 16-stone switch grinder and one 60-stone mainline rail grinder. SRS audits in August 2014 and January 2015 determined that this strategy had achieved the NRC target profiles, brought the severe RCFback into control and transitioned the rail to a preventive state. The Fortescue rail is now in excellent condition and wear rates have been significantly reduced.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".