Condition-based maintenance of wind power generation systems considering different turbine types and lead times
Bibliographic record
Abstract
Condition monitoring measurements, such as vibration data, acoustic emission data, oil analysis data, power voltage and current data, etc., can be obtained from wind turbine components and be utilised to evaluate and predict the health conditions of the components and the turbines. The objective of condition-based maintenance (CBM) is to optimise the predictive maintenance activities based on the condition monitoring and prediction information to minimise the overall costs of wind power generation systems. In existing work, all the wind turbines are assumed to be of the same type and the lead times of different components are assumed to be constant. This is not the case in many practical applications. In this paper, we develop a CBM approach for wind turbine systems considering different types of wind turbines in a wind farm and different lead times for different turbine components, which lead to more accurate modelling of CBM activities in actual wind farms. In the proposed CBM approach, we present a new CBM policy involving two design variables for each turbine type, a method for turbine failure probability evaluation considering different lead times and a CBM cost evaluation method. Numerical examples are provided to demonstrate the proposed CBM approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".