Effect of manufacturer, winding age and insulation type on stator winding partial discharge levels
Bibliographic record
Abstract
With thousands of machines monitored for as long as 25 years with the same method, on-line partial discharge (PD) testing has become a recognized, proven tool to help maintenance engineers identify which stator windings need off-line testing, inspections and/or repairs. With over 63,000 test results acquired with the same test method, what constitutes a winding with low, moderate or high PD has been identified. This paper presents tables that enable test users to easily identify with some certainty which stators are likely to suffer from groundwall insulation deterioration, with only a single measurement on a machine. The practical importance of these tables is that if one applies PD sensors to a machine and, in the first measurement, one obtains a Qm that exceeds the 90 percentile of the relevant Qm distribution, then one should be concerned enough at the PD level to take action, such as more frequent testing and/or off-line tests and inspections at the next convenient machine shutdown. Within the statistical accuracy possible with several thousands of independent results, it seems that critical PD levels only depend on operating voltage, hydrogen pressure, manufacturer, and the specific type of PD sensor and instrumentation used.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".