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Record W2161403437 · doi:10.1109/ccece.2006.277488

Analysis and Modeling of a Synchronous Machine with Structural Asymmetries

2006· article· en· W2161403437 on OpenAlexafffund
Prabhakar Neti, Subhasis Nandi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsStatorElectromagnetic coilSynchronous motorRotor (electric)Fault (geology)Computer scienceField (mathematics)VoltageSignature (topology)AsymmetryControl engineeringControl theory (sociology)EngineeringElectrical engineeringArtificial intelligencePhysicsControl (management)

Abstract

fetched live from OpenAlex

Most of the online fault diagnostic results are adversely affected by the supply unbalance and the structural asymmetries of the machine. However, in practice, it is impossible to realize a perfectly symmetrical electric motor fed by a balanced supply. Hence, in this paper, the influence of supply unbalance on the performance of synchronous machine with constructional asymmetries has been discussed. In order to conduct a theoretical study, asymmetries in the stator winding and the field winding of the machine have been considered. The field current signature analysis of the machine model with aforementioned asymmetries, with balanced and unbalanced supply, has been presented. In order to validate the theoretical results, experimental results by analyzing the field current and the voltage induced in a rotor-mounted search-coil of a synchronous machine have also been presented. The results of such a study will be extremely useful in developing unambiguous fault diagnostic tools

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.004
GPT teacher head0.223
Teacher spread0.220 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations9
Published2006
Admission routes2
Has abstractyes

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