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Record W2000705934 · doi:10.1049/iet-epa.2012.0192

Identification of spectral components in the line current of eccentric salient pole machines using a binomial series‐based inverse air‐gap function

2013· article· en· W2000705934 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

VenueIET Electric Power Applications · 2013
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSeries (stratigraphy)InverseBinomial (polynomial)Function (biology)SalientIdentification (biology)MathematicsInverse problemCurrent (fluid)Line (geometry)Binomial theoremMathematical analysisControl theory (sociology)Applied mathematicsComputer scienceEngineeringStatisticsArtificial intelligenceElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

Current literature does not provide any generalised method for specific permeance – magneto‐motive force‐based approach to predict all possible harmonic components in the air‐gap flux and line current of an eccentric salient‐pole machine. This study provides an elegant solution to express specific permeance of an eccentric reluctance synchronous machine as a summation of constant coefficient co‐sinusoidal terms. Binomial series expansion has been used to achieve this. The analysis has been validated by matching the presence of the predicted harmonic components in the stator line current by coupled magnetic circuit simulation based on modified winding function approach and experimental results. It is also shown that the effect of sensor errors, machine asymmetry, supply harmonics etc. can be minimised by residual estimation to vastly improve detection sensitivity under all load conditions. Thereafter, a procedure to identify fault type and severity has been presented.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.614

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 integrity0.0000.000
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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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