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Record W2145365342 · doi:10.1109/pes.2005.1489457

Stator fault detection in induction machines using triplen harmonics at motor terminal voltage after switch-off

2005· article· en· W2145365342 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

VenueIEEE Power Engineering Society General Meeting, 2005 · 2005
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStatorHarmonicsInduction motorVoltageControl theory (sociology)Fault (geology)EngineeringPermeanceFault detection and isolationHarmonic analysisTerminal (telecommunication)HarmonicElectrical engineeringComputer scienceElectronic engineeringAcousticsPhysicsActuatorGeologyTelecommunications

Abstract

fetched live from OpenAlex

Almost 30-40% portion of all reported induction machine faults are stator faults related. In fact, going by the number of occurrences, the position of stator faults is only second to bearing related faults. Third harmonic line currents have been reported to increase under stator fault conditions. It has been shown in this paper that they are primarily due to the interaction of saturation caused permeance wave and reverse rotating field caused due to stator faults or voltage unbalance and machine structural imbalance (usually small). Thus, voltage unbalance cannot be distinguished from stator faults. The third and the other triplen related harmonics are however found to be a very decisive indicator of the fault, if measured in the machine terminal voltages just after switch-off. The fault detection technique is independent of machine parameters and supply unbalances. Simulation as well as experimental results are presented with very few shorted turns.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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