Optimizing replacement time for mining shovel teeth using reliability analysis and Markov chain Monte Carlo simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: an approach is proposed to determine the optimum replacement time for shovel teeth; and a risk-quantification approached is developed to derive a confidence interval for replacement time. Design/methodology/approach The risk-quantification approach is based on a combination of Monte Carlo simulation and Markov chain. Monte Carlo simulation whereby the wear of shovel teeth is probabilistically monitored over time is used. Findings Results show that a proper replacement strategy has potential to increase operation efficiency and the uncertainties associated with this strategy can be managed. Research limitations/implications The failure time distribution of a tooth is assumed to remain “identically distributed and independent.” Planned tooth replacements are always done when the shovel is not in operation (e.g. between a shift change). Practical implications The proposed approach can be effectively used to determine a replacement strategy, along with the level of confidence level, for preventive maintenance planning. Originality/value The originality of the paper rests on developing a novel approach to monitor wear on mining shovels probabilistically. Uncertainty associated with production targets is quantified.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it