Joint optimal periodic and conditional maintenance strategy
Bibliographic record
Abstract
Purpose In this paper, an optimal periodic replacement strategy is proposed. This strategy suggests new items to perform replacements at failure. Preventive replacements, scheduled at instants kT (k=1, 2,…) are carried out only if the item's age exceeds a threshold to be determined. Parameters T and b are derived from an optimization model aiming to maximize the steady state availability under budgetary constraints or to minimize the expected total cost per unit of time over an infinite horizon, while the steady state availability must be higher than some given threshold. Costs and durations associated with replacement actions are supposed to be known. Design/methodology/approach Employs mathematical models to investigate the expected cost rate and the steady state availability with illustrative examples. Findings Analytical and numerical results have been obtained for a system whose lifetime is distributed according to an Erlang distribution. Practical implications The proposed strategy seems more efficient than the basic block replacement strategy aiming to maximize the steady state availability. It is also easy to implement. Originality/value This new strategy would appear to be more efficient than the previous basic block replacement strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".