ROBUST INTERVAL-BASED MINIMAX-REGRET ANALYSIS METHOD FOR FILTER MANAGEMENT OF FLUID POWER SYSTEM
Bibliographic record
Abstract
Fluid contamination is one of the main reasons for the wear failure and the related downtime in a hydraulic power system. Filters play an important role in controlling the contamination effectively, increasing the reliability of the system, and maintaining the system economically. Due to the uncertainties of system parameters, the complicated relationship among components, as well as the lack of effective approach, managing filters is becoming one of the biggest challenges for engineers and decision makers. In this study, a robust interval-based minimax-regret analysis (RIMA) method is developed for the filter management in a fluid power system (FPS) under uncertainty. The RIMA method can handle the uncertainties existed in contaminant ingressions of the system and contaminant holding capacity of filters without making assumption on probabilistic distributions for random variables. Through analyzing the system cost of all possible filter management alternatives, an interval element regret matrix can be obtained, which enables decision makers to identify the optimal filter management strategy under uncertainty. The results of a case study indicate that the reasonable solutions generated can help decision makers understand the consequence of short-term and long-term decisions, identify optimal strategies for filter allocation and selection with minimized system-maintenance cost and system-failure risk.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".