A denoising method for acoustic emission signals in wind turbine blade fatigue testing based on SVD-VMD
Bibliographic record
Abstract
To improve the accuracy and reliability of acoustic emission (AE) signal processing in wind turbine blade fatigue tests, this paper proposes a denoising method that combines singular value decomposition (SVD) and variational mode decomposition (VMD). The technique utilizes SVD to extract principal components, which are then used to determine the optimal number of VMD modes, effectively reducing mode mixing. To address the issue of mode selection, a combined metric based on permutation entropy (PE) and wavelet energy ratio is introduced to identify and eliminate noise-dominated modes, thereby achieving accurate extraction of effective modes. Comparative studies with traditional methods, such as ensemble empirical mode decomposition (EEMD) in both simulation and real fatigue test scenarios, demonstrate that the proposed approach outperforms in preserving signal features while suppressing noise. Multidimensional similarity analysis and envelope spectrum validation further confirm the effectiveness and practical value of the proposed method for the health monitoring of wind turbine blades.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".