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Record W2744324403 · doi:10.1109/intmag.2017.8008003

Acoustic noise based permanent magnet flux reduction diagnosis and current compensation in PMSM

2017· article· en· W2744324403 on OpenAlexaff
Qing Xie, Min Zhu, Jimi Tjong, Narayan C. Kar

Bibliographic record

Venue2017 IEEE International Magnetics Conference (INTERMAG) · 2017
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsFord Motor Company (Canada)University of Windsor
Fundersnot available
KeywordsCompensation (psychology)Control theory (sociology)Reduction (mathematics)MagnetNoise reductionSynchronous motorPermanent magnet synchronous motorNoise (video)TorqueComputer scienceEngineeringPhysicsElectrical engineeringMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Diagnosis for permanent magnet (PM) flux reduction is necessary for determining motor efficiency. This paper proposes a real-time diagnosis model without invading the motor structure. In this method, acoustic signals acquired with a sound intensity meter under different motor operation conditions are analyzed and processed by a wavelet packet. Moreover, the Grey-Markov Chain model is applied to estimate the flux reduction which is fed to the current compensation algorithm in the vector control drive system. The proposed model is validated in a laboratory designed permanent magnet synchronous motor (PMSM) drive system under different speeds and temperatures.

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 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 score0.686
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.326
Teacher spread0.284 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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