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Record W4210607833 · doi:10.1109/tmag.2022.3146373

Noise and Vibration Prediction of a Six-Phase IPMSM in a Single Open-Phase Failure Under a Negative Sequence Current Compensated Fault Tolerant Control Mode

2022· article· en· W4210607833 on OpenAlexaff
Pengzhao Song, Wenlong Li, Ze Li, Mohammad Sedigh Toulabi, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Magnetics · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsVibrationControl theory (sociology)TorqueHarmonicsNoise (video)Fault (geology)Phase (matter)Computer scienceFault toleranceMode (computer interface)WaveformAcousticsPhysicsVoltageControl (management)Telecommunications

Abstract

fetched live from OpenAlex

Due to a multiphase configuration, six-phase interior permanent magnet synchronous machines (IPMSMs) exhibit the inherent reliability and fault-tolerant capabilities. Using a fault-tolerant control (FTC) method, the machine is still able to deliver the desired torque under the open-phase fault with a potential degradation in its efficiency and vibration behavior. The vibroacoustic characteristics of the IPMSM under the faulty operation during the FTC mode have not been studied in the literature. To fill this knowledge gap, the simulation and experiment-based noise and vibration (NV) analysis of a six-phase IPMSM in a single open-phase condition using a negative sequence current-compensated FTC mode is studied in this article. First, the whole procedure of the NV analysis for the IPMSM model is introduced. The calculation of the electromagnetic (EM) force is then studied to explore the main excitation of the vibration in the system. After this, the NV synthesis is carried out and the simulation results of the NV under a negative sequence current-compensated FTC mode are obtained. It is found that the 28th order of the flux density spatial harmonics component influences vibration the most. Finally, the NV predictions in both healthy mode and controlled faulty mode are verified by experiments.

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 score0.662
Threshold uncertainty score0.817

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 head0.036
GPT teacher head0.279
Teacher spread0.243 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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