Optimal grouping preventive maintenance strategy for the multi-part series system under the extended warranty condition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extended warranty (EW) is an optional contract that typically provides additional guarantee to users by the manufacturer after the initial warranty period of the equipment expires. To meet users' demand for minimizing the EW cost-availability ratio during the device's EW period and protect the manufacturer's profit, this article innovatively proposes a grouping preventive maintenance (GPM) strategy for multi-part series system, based on the consideration of user and manufacturer sharing warranty cost proportionally. This strategy can integrate the preventive maintenance (PM) of each individual component in the system, thereby reducing the total number of PM times and lowering the preparation cost for PM. The decision objective of the GPM strategy is to minimize the cost-availability ratio of the system's EW, and the decision variables are the PM interval and PM improvement factor. In the case analysis, taking the power system of electric vehicles as the research object, it was found that after adopting the GPM strategy, the EW cost-availability ratio of the system reduced by 16.6%, thus verifying the superiority of the GPM strategy proposed in this paper.
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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