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Record W2516647536 · doi:10.1109/eic.2016.7548636

Rotor temperature monitoring using fiber Bragg gratings

2016· article· en· W2516647536 on OpenAlexaffabout
C. Hudon, C. Guddemi, S. Gingras, Reinaldo Corrêa Leite, Laurent Mydlarski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsMcGill UniversityHydro-Québec
Fundersnot available
KeywordsFiber Bragg gratingRotor (electric)Materials scienceStatorElectromagnetic coilOptical fiberFiber optic sensorVoltageSIGNAL (programming language)Power (physics)FiberOpticsAcousticsElectrical engineeringEngineeringComputer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Rotor temperatures of field windings are seldom measured, as permanent installation of sensors on the rotor is often difficult and can present undesired risks. However, new possibilities exist with the use of fiber Bragg gratings (FBGs). Up to 20 sensors can be mounted in series in a single fiber. Moreover, several fibers can be used in parallel, and the signal passed from the rotor to the stator through a multichannel optical rotating joint. A demonstration of such a use of FBGs has been undertaken in a Hydro-Quebec power plant, where 32 pole-to-pole connections and 28 poles were instrumented. Measurements were carried out during a heat run test and FBG results are compared with average temperature calculated from voltage and current.

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 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 score0.067
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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 teacher head, 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

Citations19
Published2016
Admission routes2
Has abstractyes

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