Dielectric testing and corona inspection of a 14.4-KV, 1800 RPM centrifugal compressor motor stator insulation system
Bibliographic record
Abstract
Factory acceptance tests (FAT) of a new high voltage rotating machine include comprehensive diagnostic tests of the finished stator winding insulation system. The end user obtains baseline data for condition monitoring performed during the machine's operating life. The design geometry of stator windings and manufacturing processes used to produce them will affect the dielectric test results. There is a significant challenge in comparing test results from different stators to obtain a statistically useful sample for an acceptance test database. This paper compares dielectric test results from the 14.4-kV windings of several new 1800 rpm, 26.1 MW (35000 hp) centrifugal compressor synchronous motor stators of identical design, built for two US chemical plants. The results are compared to those from accompanying sacrificial coils. The stator diagnostic tests include dissipation factor (DF, or tan delta), power factor tip up (PFTU, or delta tan delta), offline partial discharge (PD), corona inspection with a UV analyzer, and online PD. Sacrificial coils produced and processed alongside each stator received the specified API coil acceptance tests, plus corona inspection with a UV analyzer. PD measurements on the sample coils were repeated at elevated voltage, and the results compared to the corona inspection observations. The paper shares the comparative analysis of the stator- and coil test results as an excellent example of baseline FAT data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".