Discrete-event simulation of mine equipment systems combined with a reliability assessment model based on genetic algorithms
Bibliographic record
Abstract
The combination of simulation with the maintenance analysis of mining equipment has been proven to be an effective tool to assess the impact of equipment failures on mining equipment. Genetic algorithms have been applied to multiple areas of mine design, mostly involving optimization solutions. With regard to maintenance analysis, past research in mining focused on the design of a genetic algorithm based modelling technique that is applied to the failure data of equipment to assess the reliability of a machine under study. The objective of this research is to develop, integrate and demonstrate that a methodology involving the combination of a reliability assessment model based on genetic algorithms with a discrete-event simulation model can be an effective tool for maintenance analysis of mining equipment. The reliability assessment model based on genetic algorithms provides input in the form of times between failures (TBFs) to a discrete-event simulation model. The simulation component emulates a typical mine development cycle to analyse the effect of load-haul-dump (LHD) equipment failures on production throughput, mechanical availability and equipment utilization. Furthermore, two equivalent simulation models, built in AutoMod and Simul8, are compared to evaluate the merits of employing one simulation software package over the other. This final component of the research offers the opportunity to assess two different simulation tools for the same mining problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".