OPPORTUNISTIC MAINTENANCE OPTIMIZATION FOR WIND TURBINE SYSTEMS CONSIDERING IMPERFECT MAINTENANCE ACTIONS
Bibliographic record
Abstract
Currently corrective maintenance and time-based preventive maintenance strategies are widely used in wind power industry. However, few methods are applied to optimize these strategies. This paper aims to develop opportunistic maintenance approaches for an entire wind farm rather than individual components that most of the existing studies deal with. Furthermore, we consider imperfect actions in the preventive maintenance tasks, which address the issue that preventive maintenance do not always return components to the as-good-as-new status in practice. In this paper we propose three opportunistic maintenance optimization models, where the preventive maintenance is considered as perfect, imperfect and two-level action, respectively. Simulation methods are developed to evaluate the costs of the proposed opportunistic maintenance policies. Numerical examples are provided to demonstrate the advantage of the proposed opportunistic maintenance methods in reducing the maintenance cost. The two-level action method demonstrates to be the most cost-effective in different cost situations, while the imperfect maintenance policy, which is a simpler method, is a close second. The developed methods are expected to bring immediate benefits to wind power industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".