MétaCan
Menu
Back to cohort
Record W4231807836 · doi:10.1109/28.993182

Stator winding failures: contamination, surface discharge, tracking

2002· article· en· W4231807836 on OpenAlexaff
J.H. Dymond, N. Stranges, Karim Younsi, J.E. Hayward

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2002
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsStatorPartial dischargeFailure mechanismCorona dischargeFailure mode and effects analysisTracking (education)Void (composites)High voltageVoltageInsulation systemAutomotive engineeringMaterials scienceForensic engineeringReliability engineeringElectrical engineeringStructural engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Stator winding failures are generally attributed to groundwall or turn insulation failures. The failure mechanism involves the gradual development of a weakness in the insulation. This weakness is usually caused by a trapped void that is continuously enlarging due to ever-increasing corona or partial discharge activity. The failure mode is from the inside out. Recent studies of failures in actual machines, and of coils on voltage endurance testing, indicate that failures can occur from the outside inward. Surface contamination leads to intense surface discharge and tracking. This surface discharge can lead to rapid winding failure following the inward motion of the corona activity. This paper looks at tracking as one failure mechanism and describes a series of tests on standard insulation materials aimed at comparing the antitracking capability of the materials. The tests show that combinations of insulation materials can reduce the antitracking capacity of a rather robust insulation system and predispose it to failure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.030
GPT teacher head0.261
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicHigh voltage insulation and dielectric phenomenaFrench-language works237,207