MétaCan
Menu
Back to cohort
Record W4240350031 · doi:10.1109/28.855947

Locked-rotor and acceleration testing of large induction machines-methods, problems, and interpretation of the results

2000· article· en· W4240350031 on OpenAlexaff
J.H. Dymond, R. Ong, Paul McKenna

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2000
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsGeneral Motors (Canada)
Fundersnot available
KeywordsTorqueDynamometerVoltageRotor (electric)Control theory (sociology)Strain gaugeAccelerationBrakeAutomotive engineeringDirect torque controlComputer scienceEngineeringInduction motorElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

The measurement of locked-rotor current, torque, and power factor has been a standard test for induction machines for many years. Measurement of torque has evolved from using a brake, a dynamometer, a torque arm, and scale, through strain gauges and load cells to acceleration tests. The test must be of short duration to prevent damage to the machine and large machines present problems because of facility limitations in either kilovoltampere or torque measurement. A single test at reduced voltage when prorated to operating voltage by the square of the ratio of rated voltage to test voltage neglects the impact of saturation and results in significantly lower values of predicted torque and current. This paper discusses several methods for performing the locked-rotor and the speed-torque tests on large machines. It also discusses some of the problems associated with the test methods and shows how the tests can be performed and the results evaluated to account for saturation effects. Finally, the paper shows how to extract some machine circuit parameters from the test data.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.355

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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations16
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicElectric Motor Design and AnalysisFrench-language works237,207