Vibration Analysis of 2.3 MW Wind Turbine Operation Using the Discrete Wavelet Transform
Bibliographic record
Abstract
The vibration analysis of operational response data from a 2.3 MW wind turbine is presented. Vibration signals were acquired for two unique environmental conditions with an accelerometer mounted in the turbine tower. A Daubechies 6th order (db6) wavelet was used to perform a 12-level discrete wavelet transform (DWT) revealing trends and similarities within the signals. Full operation signals were segmented into start up and steady state periods. Analysis of turbine start up revealed a common ramping of low frequency energy on the order of rotor rotational frequency. DWT plots were also utilized to reveal high-energy response features related to the mechanical start up of the turbine. Analysis of steady state signals revealed distinct low frequency periodicity evident in the 11th (0.1776Hz) and 12th (0.0888 Hz) decomposition levels. The analysis technique performed shows promise for potential integration into comprehensive structural health monitoring schemes designed to reduce downtime and improve the reliability of commercial wind turbines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".