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Record W4381730705 · doi:10.1109/tmag.2023.3288831

Comparative Analysis of Noise and Vibration for Dual Three-Phase IPMSM Under Healthy and Multi-Phase Open-Circuit Fault Operations

2023· article· en· W4381730705 on OpenAlexaff
Pengzhao Song, Wenlong Li, Ze Li, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Magnetics · 2023
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsVibrationComputer scienceFault (geology)TorqueNoise (video)Magnetomotive forceMagnetFault toleranceElectromagnetic coilControl theory (sociology)Phase (matter)Dual (grammatical number)AcousticsElectrical engineeringPhysicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The dual three-phase interior permanent magnet synchronous machines (IPMSMs) exhibit excellent fault-tolerant capabilities due to their multi-phase winding configurations. Under certain winding failure circumstances, by using the fault tolerant control (FTC) to reconstruct the normal rotating magnetomotive force, the machine can still provide the expected comparable torque as the healthy mode does. However, in the existing literature, limited attention was paid to the noise and vibration (NV) problems targeting the dual three-phase IPMSMs in FTC operation. To fill this knowledge gap, this article conducts a quantitative comparison on the NV performance of a dual three-phase IPMSM under healthy and multi-phase open-circuit fault conditions under FTC. The comparative studies involve radial electromagnetic (EM) force, acceleration distribution, sound pressure level (SPL) as well as machine housing deformation, which are analyzed under healthy and multi-phase open-circuit fault operations. Based on our exclusive findings, after applying the FTC, even though the machine can deliver the desired EM performance, the maximum deformation and SPL of the IPMSM can be mitigated by 12.9% and 16.9%, respectively, compared to the ones under the faulty condition without FTC.

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 score0.794
Threshold uncertainty score0.663

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 head0.071
GPT teacher head0.342
Teacher spread0.270 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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