Selective maintenance modeling for a multistate system with multistate components under imperfect maintenance
Bibliographic record
Abstract
In many industrial environments, maintenance is performed during successive mission breaks. In these conditions, it may not be feasible to perform all possible maintenance actions due to limited maintenance resources such as time, budget, repairman availability, etc. A subset of maintenance actions is then performed on selected components such that the system is able to meet the next mission requirement. Such a maintenance policy is called selective maintenance. In this article, a selective maintenance strategy is developed for a MultiState System (MSS). The system can have several finite levels of performance in an MSS. Previous studies on selective maintenance have solely focused on MSSs with binary components. However, components in an MSS may be in more than two possible states. Hence, a series-parallel MSS that consists of multistate components is considered in this article. Imperfect maintenance of a component is considered to be a maintenance option, along with the replacement and the do-nothing options. Maintenance resources need to be allocated such that maximum system reliability during the next mission is ensured. A universal generating function is used to determine system reliability. An illustrative example is presented that depicts the advantages of utilizing imperfect maintenance/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 | 0.001 |
| 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 | 0.001 | 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".