Using magnetic flux monitoring to detect synchronous machine rotor winding shorts
Bibliographic record
Abstract
The rotor windings of synchronous motors and generators are usually very reliable. However, the turn insulation in such machines will eventually degrade and puncture due to thermal aging, load cycling and/or contamination. Although turn shorts do not directly lead to machine failure, they can lead to high bearing vibration, may damage synchronizing systems within brushless motors and may limit output. Off-line tests are available to detect rotor winding shorted turns, however they may be unreliable since the rotor is not spinning for the test and if only a few shorts are present, the shorts may disappear once the rotor is spinning (or vice versa). With funding from the US Electric Power Research Institute, a new type of on-line magnetic flux test has been developed that is suitable for salient pole machines, and in particular the salient pole rotors in 4 (or more) pole hydrogenerators and synchronous compressor motors that are widely used for air separation. The test requires retrofitting a small magnetic flux probe to the stator core and instrumentation to interpret the small voltage signals that are measured from the probe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".