Infrared thermography inspection of glass reinforced plastic (GRP) wind turbine blades and the concept of an automated scanning device
Bibliographic record
Abstract
Infrared thermography techniques have been used for many years in the non-destructive testing and evaluation (NDT and E) of materials and structures. The main advantage of thermography over classical NDT techniques resides in the possibility of inspecting large areas in a fast and safe manner without needing to have access to both sides of the component. Nonetheless infrared thermography is limited to the detection of relatively shallow defects (a few millimetres under the surface), since it is affected by 3D heat diffusion. However, the most common types of anomalies found on composites, such as GRP wind turbine blades are delaminations, disbonds, water ingress, node failure and core crushing, and can be effectively detected and sometimes quantified using active thermographic techniques. This research work presents the use of infrared thermography on glass reinforced plastic (GRP) wind turbine blades assessment. Finally, the development of an autonomous, novel and lightweight multi-axis scanning system, as a concept, deploying in situ thermography NDT is also presented, with the intention of developing maximisation of the blade area coverage in a single run, at a known sensitivity, with the utilisation of the minimum number of system degrees of freedom and the maximum repeatability, as well as positional accuracy possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".