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Record W1983052884 · doi:10.1109/icelmach.2012.6350097

Current residue based load independent eccentricity detection in salient pole synchronous machines

2012· article· en· W1983052884 on OpenAlexaff
T. Ilamparithi, S. Nandi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSalientEccentricity (behavior)ResidualStatorCurrent (fluid)Computer scienceFault detection and isolationControl theory (sociology)Synchronous motorSensitivity (control systems)Electronic engineeringAlgorithmEngineeringArtificial intelligenceElectrical engineeringActuator

Abstract

fetched live from OpenAlex

In this paper a load independent eccentricity detection scheme based on current signature analysis is proposed. The main idea of the method is to compute the residual current estimates of the fault specific frequency components and eliminate them from the current spectrum of the motor. Such a method has many advantages in addition to load independent detection such as low cost, high sensitivity to detect low level eccentricity faults, minimal hardware requirement etc. Also the scheme is non-invasive in nature and can be employed for online detection. For the first time both stator line current and field current of the synchronous machine have been used independently to diagnose eccentricity. Experiments conducted on a 3 hp salient pole synchronous motor have been used to validate the proposed technique.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.879

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 designObservational
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

Citations4
Published2012
Admission routes1
Has abstractyes

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