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Record W4288771473 · doi:10.1109/jestie.2022.3195094

Induction Machine Emulation for Variable Frequency Drive Converter Faults

2022· article· en· W4288771473 on OpenAlexaff
Gayatri Tanuku, Pragasen Pillay

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Industrial Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsEmulationUpgradeFault (geology)VoltageEngineeringComputer scienceElectronic engineeringEmbedded systemElectrical engineering

Abstract

fetched live from OpenAlex

Voltage in current out models are commonly used for induction machine (IM) emulation. This model does not provide the information of back electromotive force (EMF) and hence cannot be used for open-circuit fault emulation. A current in voltage out model (CIVO) is an alternate IM model where it provides back EMF information. However, the CIVO model cannot be used directly with the conventional emulator configuration. Hence, in this article, a new emulator configuration has been proposed to accommodate the CIVO model. The model is used in a new emulator configuration by incorporating physical emulator parameters. The article further discusses the upgrade of the model considering saturation nonlinearities. The emulator employing such a model mimics the real IM back EMF with high accuracy. The proposed emulator is tested for different gate driver faults, such as single switch and multiple switch open faults, starting and speed changing conditions. The experimental results are validated with a 5 hp machine. Different fault signatures are also identified with the help of the emulator to help with fault diagnosis and localization.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.517

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 integrity0.0000.001
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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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