Anomaly Detection in the Vibration of Wind Turbine Blades using Gaussian Process Regression
Bibliographic record
Abstract
The world is now moving towards a shift from traditional energy sources to renewable energy, like, wind power.Wind turbine is being manufactured in larger sizes today to harvest more energy although this leads to several technical challenges.The focus of this thesis is on blade-related damages.The aeroelastic behaviour of the blades is investigated by developing reliable models in OpenFAST (wind turbine simulation tool).In this work, several timeseries data are generated through OpenFAST considering healthy and damaged cases, where the damage is artificially introduced by decreasing the structural stiffness of one blade at three critical locations along the blade span.The goal of the present study is to develop a Gaussian process regression model using evidence (marginal likelihood) function to identify the damaged measurements.This resulted in a successful detection of the anomalous group of samples using the developed anomaly detection algorithm under possible and impossible visual detection scenarios.I would like to thank my supervisor, Prof.
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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) | 0.000 | 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 | 0.001 |
| 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 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".