Aggregate reliability analysis of wind turbine generators
Bibliographic record
Abstract
In North America, many utility‐scale turbines are approaching, or are beyond the half‐way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimise the life and value of their farm assets. In this study, data records from a wind farm have been used to estimate the reliability of wind turbine (WT) generators. For this study, non‐parametric life data analysis, Weibull Standard Folio life data analysis, and ALTA Standard Folio life data analysis have been used to predict the reliability of the generators. The naive prediction interval procedure also has been used here to provide an approximate range for the remaining life of each generator. This study provides some insight into how reliable a subset of WT generators is and the lifetime distribution of individual generators. These outcomes may be leveraged further by the research community for companion applications like prognostic maintenance and investment decision support systems. This study also begins to investigate how electrical loads may influence turbine generator reliability. The work also illustrates a valuable example of how to estimate component remaining useful life based on truncated/limited data records.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".