Cost-availability ratio modeling of two-dimensional extended warranty for multi-component systems with fault correlation
Bibliographic record
Abstract
The existing research on multi-component systems mostly assumes that the faults between components are independent and ignores their practical correlation, which will inevitably affect the calculation of system warranty cost and warranty availability. In order to quantitatively analyze the impact of fault independence and fault correlation between components on the minimum two-dimensional extended warranty (EW) cost-availability ratio of the system, this paper establishes a two-dimensional EW cost model and availability model for multi-component systems considering fault correlation based on incomplete periodic preventive maintenance (PM), and forms a warranty cost-availability ratio model accordingly. Subsequently, the artificial bee colony (ABC) intelligent optimization algorithm was introduced to solve the model, and a case study was conducted on the transmission system of a certain new energy vehicle. Through numerical comparison, it was found that considering fault correlation compared to the assumption of fault independence would increase the warranty cost-availability ratio of the system by 20%, providing more practical warranty references for users and manufacturers, and verifying the superiority of the model. Finally, a sensitivity analysis was conducted on the model to guide its more effective implementation and application.
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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 | 0.003 |
| 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 | 0.002 |
| 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".