Bibliographic record
Abstract
A typical mining company has three important assets: the human labor-force, the orebody, and the equipment. Trucks, excavators, drilling machines, crushers, grinders, classifiers, and concentrators comprise the equipment. Mining operations that want to take advantage of economies of scale have huge equipment fleet, and the worth of the equipment may easily exceed a hundred million dollars. The reliability and availability of this equipment play critical roles in increasing the efficiency and productivity of a mining operation. The losses associated with low performance or unavailability can be significant. The contribution of this thesis can be divided into two sections. The first part proposes an effective maintenance management approach to be used in the mining industry such that equipment availability and reliability are improved. The second section investigates the reduction effect on greenhouse gas emissions due to maintenance activities since most of these enormous equipment fleets used in mining are still diesel-powered.Using failure data of a mining truck fleet in an open-pit Canadian mining operation, a case study is conducted to determine the optimal inspection intervals based on the desired reliability level to detect potential catastrophic failures. Next, a preventive maintenance scheduling plan based on systems’ rejuvenation after each repair is explored for mining equipment. Finally, the relationship between equipment reliability and CO2 emissions is quantified and a regression model to predict emission is developed. The research outcomes show that the proposed approach has the potential to increase the efficiency and productivity of the mining equipment and can be used to contribute to equipment management towards more sustainable mining operations
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".