Analysis of a Partial Discharge Database to Determine When Stator Winding Insulation Maintenance is Needed
Bibliographic record
Abstract
On-line partial discharge testing and monitoring have been widely applied by petrochemical industries on large motors and generators to determine the need for maintenance of the stator winding insulation system. Over the past 20 years, a single consistent method has been used to collect on-line PD data from over 22 000 motors and generators equipped with the required sensors. Of these, PD data from 8500 machines collected to the end of 2021 have been assembled into a single database, along with machine ratings and machine operating data. For each machine, the PD magnitude for each phase from the most recent test when the motor or generator was operating at normal load and stator winding operating temperature was statistically analyzed. The cumulative probability of occurrence for any PD activity level for any particular machine rating, manufacturing method and winding design etc. could then be produced. It has become clear that the probability distributions for different stator winding operating voltages produce statistically significant distributions for machines of various voltage ratings. Over the years, these probability tables have been correlated with visual inspections of hundreds of stator windings as well as off-line test results. This analysis indicates that when the PD magnitude is higher than about 90% of similar machines tested with the same method, then there is a very high probability of significant stator winding aging. This, combined with the evolution of PD over time, can be used to determine when maintenance is advisable.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".