An improved fuzzy programming model with an L—R fuzzy number for filter management strategies in fluid power systems under uncertainty
Bibliographic record
Abstract
A fuzzy non-linear programming model is developed for the assessment of filter allocation and replacement strategies in fluid power systems (FPSs) under uncertainty. It can not only handle uncertainties expressed as L—R fuzzy numbers, but also enhance the system robustness by transforming the fuzzy inequalities into inclusive constraints. In modelling formulation, theory of contamination wear is introduced to reflect the interactions between system performance and system contamination level. The developed method has been applied to the planning of filter allocation and replacement for an FPS with a bypass filtration system, and a piecewise linearization approach is proposed for solving model non-linearities. Three different contaminant ingression rates are examined based on a number of filter-installation scenarios. The generated solutions can be used for providing guidance to decision makers to identify better contamination control plans for achieving a minimized system cost and system-failure risk.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".