An IPINP model for the assessment of filter allocation and replacement strategies in a hydraulic contamination control system under uncertainty
Bibliographic record
Abstract
An interval-parameter integer non-linear programming (IPINP) model is developed for the assessment of filter allocation and replacement strategies in a hydraulic contamination control system under uncertainty. The IPINP can handle uncertainties expressed as interval values that exist in the left- and right-hand sides of constraints as well as in the objective function. A piecewise linearization approach is proposed to solve the IPINP model, which has advantages in identifying global optimum and is associated with low computational efforts for an uncertain non-linear programme. The developed method has been applied to a case of planning filter allocation and replacement strategies under uncertainty for a fluid power system (FPS) with a single circuit. Three different contaminant ingression/generation rates are examined based on several filter-installation scenarios. The combination of low-costing suction and return filters exhibits excellent contaminant resistibility along with the optimum replacement periods of filter elements and operation costs under the low and medium contaminant ingression/generation level. The solutions can be used for generating a range of decision alternatives and thus help the decision-makers to identify desired filter allocation and replacement plan with a minimized operation cost and a minimized system-failure risk for FPS.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".