A New Composite Allocation Method on Balance of Reliability and Maintainability Index With the Goal of Availability
Bibliographic record
Abstract
This paper proposes a new composite allocation method, which is composed by improved Fuzzy-AHP allocation method, old system data correction allocation method, and optimization allocation method. The objective of the new method is to minimize the system cost and allocate the reliability index and maintenance index of unit with the goal of system availability and the balance between them. For the solution of optimization problem in this paper, in order to prevent local optimum, improve the convergence efficiency and get the satisfied optimal solution, the improved GA method is put forward in this paper. First, the reliability index allocation method is proposed by the combination of optimization allocation method with the objective of minimum cost, the improved Fuzzy-AHP method with the consideration of the experts’ expectations, and the old system data correction allocation method. Then, based on constraints of the unit reliability allocation index and system availability index, the maintain-ability allocation method is proposed considering the minimum maintenance cost. In addition, the process of this new composite allocation method is formulated in this paper. Finally, the system cost, reliability index, and maintenance index of an integrated transmission device of an armored vehicle are allocated by this new composite allocation method. The result analysis shows that the allocation result of this new composite allocation method is reasonable and has engineering applicability. All in all, this new composite allocation method not only synthetically considers experts’ expectations on the new system, system cost and system reliability baseline information, but also associate the reliability index and maintenance index with the goal of system availability. In addition, this paper provides a new approach for reliability index & maintenance index of complex repairable systems in the early stages of product design.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".