Post-mortem dissection of stator bars and coils
Bibliographic record
Abstract
In order to improve our understanding of failure mechanisms and their relative risk, dissection was performed on stator bars (coils) extracted from a generator after a failure. In combination with other non-destructive diagnostic tools such as partial discharge and dielectric response measurements, the information obtained from dissection reveals many symptoms which can be used to identify the root cause of the failure and to assess the progression rate of the different insulation degradation mechanisms. As the failed bar or coil is often jumped to resume operation, these key results are essential to decide on the best maintenance actions for this generator and other units of the same design to reduce further future failure risks in the plant. Dissection has been performed for years, and everyone has built his own expertise and defined criteria for what is normal or not for every aspect of the bar (coil) construction. This paper describes a procedure that relies on Hydro-Québec's own experience in the dissection of stator bars (coils). A case study of in-service failure illustrates the correlation between dissection and other diagnostic test results. Examples of insulation degradation symptoms are presented and a quantification of some of them is proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".