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Record W3043770655 · doi:10.1016/j.promfg.2020.06.011

Dynamic modeling of a planetary gear system with sun gear crack under gravity and carrier-ring clearance

2020· article· en· W3043770655 on OpenAlexaff
Xianhua Chen, Yuejian Chen, Ming J. Zuo

Bibliographic record

VenueProcedia Manufacturing · 2020
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRing (chemistry)EngineeringAerospace engineeringPhysicsAstrobiologyChemistry

Abstract

fetched live from OpenAlex

The large load capacity, compact size and high-power density have made planetary gearboxes widely applied in heavy machinery such as wind turbines. Although well designed, planetary gearboxes are vulnerable to fatigue crack because of harsh working environment such as high loads. Fatigue crack may eventually cause failures of planetary gearboxes if not detected early. This study investigates the dynamics of a planetary gear system with a crack in a sun gear tooth. This paper considers the gravity effect, carrier-ring bearings clearance, gyroscopic and centrifugal forces that reported studies have not considered together yet. Simulation results reveal that gravity produces a sinusoidal shift in the dynamic response of a planetary gearbox in the time domain. The carrier-ring bearing clearance causes crack-induced impulses to be more apparent. These results can help researchers develop more effective fault diagnosis strategies especially for early crack in the sun gear.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.178
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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