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Comparative Analysis of Noise and Vibration for Dual Three-phase IPMSM under Healthy and Multi-phase Open-circuit Fault Operations

2023· article· en· W4386429229 on OpenAlexaff
Pengzhao Song, Wenlong Li, Ze Li, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsVibrationStatorFault (geology)TorqueControl theory (sociology)Noise (video)Computer scienceElectromagnetic coilFault toleranceMagnetAcousticsEngineeringPhysicsElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The dual three-phase interior permanent magnet synchronous machines (IPMSMs) exhibit excellent fault-tolerant capabilities due to the multiphase winding configuration. Under certain winding failure circumstances, the machine can still provide the expectedly comparable torque as the healthy mode using the fault tolerant control (FTC) to reconstruct the normal rotating magnetomotive force. However, limited attention was paid to the noise and vibration (NV) problems of the dual three-phase IPMSMs in FTC operation. This digest conducts a quantitative comparison on the NV performance of a laboratory dual three-phase IPMSM under healthy and multiphase open-circuit fault conditions controlled by our newly proposed FTC algorithm. The comparative studies involve radial electromagnetic force, acceleration distribution, sound pressure level as well as stator deformation, which are analyzed under both conditions. Based on the research, after applying the FTC, the maximum deformation and sound pressure level of the IPMSM can be mitigated by 6.9% and 12.5%, respectively, compared to ones under fault condition.

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: none
Teacher disagreement score0.604
Threshold uncertainty score0.512

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 head0.122
GPT teacher head0.375
Teacher spread0.253 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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