Electromagnetic modelling and detection of buried stator core faults
Bibliographic record
Abstract
Interlamination insulation faults in the stator cores of large electrical machines can damage both winding insulation and stator core, thus confidence in electromagnetic test results is important. They may be validated by finite element (FE) methods, however the 3D models required for short faults are computationally challenged by laminated structures, requiring approximations. A homogenised 3D FE model was used to model faults buried in the teeth and yoke of the core, with a new experimental methodology developed to calibrate fault currents. Limitations were identified in modelling just a core section due to images and the constraint of axial packet air gaps on fault flux dispersion. A system of transverse 2D FE models of the principal fault flux paths in the core were constructed to estimate the differential impact on fault signals by the air gap presence and applied to the 3D FE model. Together with corrections for images this gave close predictions of experimental results, supporting the validity of the model. The verified electromagnetic test results now permit assessment of the threat that a detected buried fault presents.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".