A Methodology for Maintenance Evaluation and Improvement of Repairable Systems in a Mine
Bibliographic record
Abstract
In this thesis, a maintenance evaluation and improvement methodology is presented, which makes use of maintenance data to determine failure characteristics of repairable systems and the effectiveness of maintenance policies being conducted on them. The objective is to provide a way in which maintenance data can be collected, organized, cleaned and formatted to provide information on component failures analytics, system availability and utilization so as to determine flaws in maintenance strategies. The methodology also provides context for the study of maintenance effectiveness, and synthesizes its importance within the grander scheme of maintenance optimization of repairable systems. We consider a repairable system whose failures follow a Non-Homogenous Poisson Process (NHPP) with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. We assume the effects of different preventive maintenance on the system are not identical, and estimate the parameters of the failure process as well as the effects of preventive maintenance. Ultimately, the methodology serves to guide maintenance designers in measuring the effectiveness of current maintenance policies and providing granular analysis on current failure trends to arrive at data-driven options for maintenance improvement. The proposed methodology was applied to a real case study of four AC-powered dump trucks used at an underground mine in Sudbury, Canada.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".