Maintenance policies with minimal repair and replacement on failures: analysis and comparison
Why this work is in the frame
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Bibliographic record
Abstract
The main objective of a maintenance policy consists of conducting maintenance actions at lower costs. This paper proposes an approach for comparing numerically three maintenance strategies, involving minimal repairs at failure, replacement with complete renewal only at the first failure, and replacement with complete renewal at each failure. These strategies are integrated into a modified block replacement policy that includes corrective and preventive maintenances. The approach proceeds by presenting the mathematical models at the component level and at the system level. As the renewal function for generalised Weibull distributions is impossible to obtain, a novel asymptotic algorithm is introduced for estimating the replacements number. However, a multi-component industrial example is proposed for selecting the strategy that minimises the maintenance costs. A sensitivity analysis is performed for comparing an opportunistic maintenance policy with the proposed replacement policy to check if substantial cost reduction still possible. The experiment results show clearly that the third strategy is the most efficient and reduces maintenance costs to a very low level. Finally, we think that the developed study provides a flexible and less costly solution to deal with maintenance decision-making for systems that do not have modern technological equipment to collect data from system breakdowns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it