Condition-based Selective Maintenance for Multicomponent Systems Under Environmental and Energy Considerations
Why this work is in the frame
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Bibliographic record
Abstract
This work develops a new variant of selective maintenance (SM) optimization model for multicomponent systems running multiple alternating sequences of missions and breaks. A component deteriorates randomly and fails when the corresponding failure threshold is exceeded. Components' failures impact the quality of the environment and increase the energy consumption. Thus, failures induce penalty costs. Improving the system reliability during the following mission is achieved by performing maintenance activities on its elements during the breaks. A condition-based SM optimization problem (CBSMP)is developed to minimize the total expected cost subject to the limited break durations and required reliability for the next mission. A model's solution determines an optimal SM plan which minimize the total expected cost resulting from inspection, maintenance, and costs due to impact of components' failures on the environment and energy requirements. The proposed approach is tested on a numerical example.
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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 | 0.000 | 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