The development of a representative multidimensional transient duty cycle for in-service switcher locomotives
Bibliographic record
Abstract
Switcher locomotives operate in railway yards where they shunt railcars and assemble trains. Shunting railcars requires frequent aggressive acceleration and deceleration events in order for the locomotive to push or pull railcars onto specific tracks. As a result, switcher locomotives rarely sustain tractive power for any significant period of time. Given that all switchers in North America rely on diesel-electric propulsion; the result is rapid and frequent transitions in engine power leading to a very low engine efficiency and high levels of emissions. Any attempt to quantify or remedy these issues will face a lack of a representative profile or test cycle. A locomotive duty cycle is a breakdown of time spent at each power level of the locomotive’s engine. A major drawback of current duty cycles is that they only account for steady power. Such cycles are not representative of real switcher locomotive operation. This paper presents a real-world transient duty cycle for switcher locomotives that accounts for the rapid power transitions and is argued to be more statistically representative of actual operations. The methodology adopted relies on real-time data collection, microtrip based trip segmentation, and a finite mixture model-based clustering algorithm. Measurements were collected on a EMD 16-645 GP9 locomotive. The duty cycle developed herein is representative of switching operations in Southern Railway of British Columbia’s New Westminster Yard as an example of the methodology which can be repeated in other cases as well.
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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 | 0.000 |
| 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".