Detection of insulation defects on generator bars and coils using an instrumented impact test
Bibliographic record
Abstract
The methodology used by the industry to identify defects in the insulation system of generator bars and coils is based on a subjective acoustic detection test, commonly known as tap test. These defects are in the form of delamination between the insulation layers, decohesion between the insulation and copper or cavities. However, the tap test can be adapted using well-known scientific principles in modal analysis of structures and in high frequency acoustic emission detection. It is therefore possible to propose an instrumented methodology currently followed to carry out this test more objectively. This paper presents a rapid review of modal analysis principles useful for defects detection on structure. Also, based on this knowledge, an impact test method is proposed for generator stator bars and coils. This methodology offers a stricter and more defined procedure than the usual tap test as well as more objective criteria for localizing the defects. It required the use of an instrumented impact hammer and a microphone. In parallel, another test is proposed using an acoustic camera to obtain a visual localisation of potential defect location based on high frequency emission. This paper presents the results of both tests perform on a single hydrogenerator stator coil and correlate to tap test and microscope examination. The acoustic camera test results are well correlated with delamination detected with the tap test and confirmed with microscope examination. More investigation is in progress to correlate insulation defects with the instrumented impact test results.
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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".