Reliability Optimization Allocation Method for Multifunction Systems Based on Goal Oriented Methodology
Bibliographic record
Abstract
This paper proposes a new reliability optimization allocation for multifunction systems based on GO methodology. First, two constraints functions are proposed, which are unit reliability constraint function and system reliability constraint function, respectively. The unit reliability constraint function consists of allocated reliability index of unit and the range of reliability index for unit. And the system reliability constraint function consists of the target reliability index of system, and the predicted reliability index of system obtained by using GO method and allocated reliability index of unit. Then, the objective function of optimization allocation problem is established to describe the system cost minimization taking into consideration costs of unit redesigned and unit selected versions. Based on above, the mathematic model of reliability optimization allocation problem for complex multifunction systems is established. In addition, an improved genetic algorithm is presented to solve this mathematic model. Furthermore, the process of the new reliability optimization allocation method for complex multifunction systems is formulated. Finally, the new method is applied in reliability optimization allocation of Power-shift Steering Transmission whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indexes of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems.
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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".