Selective maintenance for binary systems using age-based imperfect repair model
Bibliographic record
Abstract
In many applications, the break between successive missions provides an opportunity to perform maintenance under limited resources. Such maintenance policy is called selective maintenance. Traditionally, it was assumed that a component after repair may be as good as new or as bad as old. However, maintenance can bring a component in between these two extreme cases as well. This maintenance policy is called imperfect repair. Selective maintenance optimization under imperfect repair is studied in this paper. Age reduction model is used to represent imperfect repair for selective maintenance. It is suggested in [1] that age reduction factor depends on the maintenance cost, and a constant is used to reflect whether a component is relatively young or old. In this paper, a formulation is used for this characteristic constant which depends on the effective age of the component. It is shown that the formulation helps in establishing a relationship between age reduction factor, cost of maintenance, and effective age of the component. Also, advantage of selective maintenance with imperfect repair is shown and results are compared with the case when only minimal repair and replacement are considered as repair options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".