Maintenance Optimization of Multi-Component Systems Subjected to $s$-Dependent Competing Risks and Imperfect Maintenance
Bibliographic record
Abstract
This paper develops a novelreliability-based maintenance optimization approach in multi-componentsrepairable system (MCRS) subjected to both$s$-dependent competing risks andimperfect maintenance. The system operates a sequence of preventive maintenance(PM) cycles at the end of which a preventive replacement of the system iscarried out. Duration of each PM cycle is determined by a reliability thresholdwhich triggers either an imperfect PM or a preventive replacement; there are asmuch PM cycles as reliability thresholds. The objective of the proposedmaintenance approach is to determine the joint optimal reliability thresholdsand the number of PM cycles that minimize the total expected maintenance andbreakdown cost rate in the infinite time horizon. Conditions to derive optimalsolutions are formally established and discussed. Given the complexity of theresulting optimization problem, a fix-and-optimize numerical algorithm usingthe gradient descent method is developed. The numerical results obtained fromthe case study demonstrate the accuracy and the added value of the proposedapproach. These results clearly show that it is not only cost-effective butalso adaptive and allow then to increase the system availability. Indeed,except for the last PM cycle, it is shown that the optimal reliabilitythreshold increases by the increasing of the number of PM cycles.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".