VIBRATION TRANSMISSIBILITY AS A METHOD OF DAMAGE DETECTION ON HORIZONTAL AXIS WIND TURBINE BLADES
Bibliographic record
Abstract
Structural health monitoring of horizontal axis wind turbine blades is challenging due to their large size and limited in-service accessibility. Given the harsh environment that wind turbines operate in, the blades are prone to damage throughout their working life. This paper examines the transmissibility of vibration response among embedded sensors over time as a method of damage characterization. After damage occurs, the sensors closest to the new defect will experience the highest change in transmissibility compared to baseline readings. The defect can therefore be identified and located with this method. When assessed at the natural frequencies of the blade, the response transmissibility between two sensors is independent of applied force and magnitude. This makes vibration transmissibility an ideal method for inspecting wind turbine blades that are subject to varying wind speed and direction. A blade was designed based on the NREL IEA Wind -15MW offshore reference wind turbine, with modifications made to fabricate a scaled 3D printed model for testing. The blade was outfitted with MEMs accelerometers to measure acceleration and fiber Bragg gratings (FBGs) to measure strain. To test the transmissibility concept prior to experimental testing, a finite element model was developed to simulate acceleration and strain transmissibility on a damaged and undamaged blade with random force inputs. This model was able to demonstrate that a 5mm transverse crack was visible across the blade when examined at the natural frequencies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".