Black-Out Test Versus UV Camera for Corona Inspection of HV Motor Stator Endwindings
Bibliographic record
Abstract
As part of a factory acceptance test program for high-voltage (HV) motors, end users sometimes specify a black-out test. This is a traditional offline inspection where the stator is placed in complete darkness, each phase is energized to 115% of rated line-to-neutral voltage, and both ends of the stator are observed to determine the presence, location, and severity of endwinding surface partial discharges (PDs). The setup for the test may be complex and risky due to the need for observers to be standing in darkness close to the energized parts of the stator. The test results are qualitative and strongly depend on the observer's eyesight and individual perception. A safer and more accurate alternative is to use an ultraviolet (UV) camera or viewer. The observed PD activity may be observed in ambient lighting, recorded, and quantified through simultaneous offline PD measurements. This paper describes the two inspection techniques and presents experimental validation of the UV corona camera inspection method as a suitable replacement for a black-out test. Sample 13.8-kV coils were wound in a fixture simulating their relationship in a stator winding and subjected to high-potential tests while observed under black-out conditions and with a UV corona camera in ambient lighting. The stator windings from two HV compressor motors were inspected using the same camera. Recorded images of the observed discharges and measured PD activity in the sample coils and stator winding were used to compare the evaluation by each test method.
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 | 0.000 | 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 | 0.001 | 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".