Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Maintenance scheduling is essential and crucial for wind turbines to avoid breakdowns and reduce maintenance costs. Many maintenance models have been developed for wind turbines' maintenance planning, such as corrective, preventive and predictive maintenance. Due to communities' dependence on wind turbines for electricity needs, preventative maintenance is the most widely used method for maintenance scheduling. The downside to using this approach is that preventive maintenance is often done in fixed intervals, which is inefficient. In this paper, a more detailed maintenance plan for a 2MW wind turbine has been developed. The paper's focus is to minimize a wind turbine's maintenance cost based on a wind turbine's reliability model. This study uses a two-layer optimization framework: Fibonacci and Genetic Algorithm (GA). The first layer in the optimization method (Fibonacci) finds the optimal number of preventive maintenance required for the system. In the second layer, the optimal times for preventative maintenance and optimal components to maintain have been determined to minimize maintenance costs. The Monte Carlo simulation estimates wind turbine component failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the overall corrective and preventive maintenance costs during the system's lifetime. Finally, an optimal preventive maintenance schedule is proposed for a 2MW wind turbine using the presented method. The method used in this paper can be expanded to a wind farm or similar engineering systems.
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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 | 0.000 | 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 | 0.000 |
| 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 it