A Study of Digging Productivity of an Electric Rope Shovel for Different Operators
Bibliographic record
Abstract
A performance monitoring study of an electric rope shovel operating in an open pit coal mine was conducted. As the mining industry moves toward higher productivity, profitability and predictability, the need for more reliable, productive and efficient mining shovels increases. Consequently, it is critical to study the productivity of these machines and to understand the effect of different operational parameters on that. In this paper a clustering analysis is performed to classify shovel digging effort and behaviour based on digging energy, dig time and payload per pass. Then the influence of the operator on the digging efficiency and productivity of the machine is analyzed with a focus on operator technique during digging. A statistical analysis is conducted on different cycle time components (dig time, swing time, return time) for different operators. In addition to time components, swing and return angles as well as loading rate and mucking rate are observed and analyzed. The results of this study help to understand the effect of different operators on the digging productivity of the shovel and then to set the best operator practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".