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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 OpenAlexaff
Simon Bernier, Arezki Merkhouf, Kamal Al‐Haddad

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2024
Admission routes1
Has abstractyes

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