Objective methods to interpret partial discharge data on rotating machines
Bibliographic record
Abstract
Partial discharge (PD) measurements have long been used to assess the condition of the electrical insulation in motors and generators rated 3.3 kV and above. There are many ways to measure PD during normal service of the motor or generator. Unfortunately most of the measurement methods mix stator PD with electrical interference signals from poor electrical connections, power tool operation, corona from transmission lines, etc. The result can be false indications of stator winding problems, reducing confidence in PD measurements. Another issue with on-line PD testing is interpretation: i.e. identifying which machines are in good condition and which need maintenance. In the past decade, a database of over 60,000 test results has been assembled. In hundreds of machines, the condition of the insulation determined by a visual inspection has been compared to the PD levels. The result is a table that provides an objective means of determining the stator insulation condition relative to other similar machines. Analysis of the results also shows that there are significant differences in PD activity between manufacturers. This paper gives a review of the methods that can reduce the risk of false indications, thus making the measurement less subjective
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".