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Record W4401507983 · doi:10.1109/tia.2024.3441519

Diagnosis of Multiple Defects Within Large Hydroelectric Generator Using Stray Flux and Air Gap (Distance and Flux) Measurements

2024· article· en· W4401507983 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsÉcole de Technologie SupérieureHydro-Québec
Fundersnot available
KeywordsFlux (metallurgy)Magnetic fluxAir gap (plumbing)Generator (circuit theory)HydroelectricityElectrical engineeringElectric generatorMaterials scienceEnvironmental sciencePhysicsEngineeringMetallurgyMagnetic fieldPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

This paper presents the analysis of both stray and air gap magnetic flux measurements within large hydroelectric generators. The study demonstrates the similarities and differences between these two types of measurements when used in the development of diagnosis and remedial strategies. The radial and tangential components of the stray flux are also shown at different measurement locations around the hydroelectric generators. The measurement approach employed in this study was able to detect electrical and mechanical failure mechanisms in hydroelectric generators such as rotor eccentricity (static or dynamic), rotor ellipticity and interturn short circuit (ITSC)in the rotor windings (Bernier et al. 2023). The approach was validated using measurements taken within nine large hydroelectric generators operating at different powers and load conditions.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.922

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.272
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