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Dynamic Modeling of a Planetary Gearbox with Sun Gear Crack and Bearing Clearance

2020· article· en· W3090015679 on OpenAlexaff
Xianhua Chen, Xingkai Yang, Ming J. Zuo, Zhigang Tian

Bibliographic record

Venue2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM) · 2020
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBearing (navigation)TorqueDisplacement (psychology)Automotive engineeringNon-circular gearStructural engineeringPlanetDynamic load testingDynamic simulationEngineeringFriction torqueMain bearingComputer scienceMechanical engineeringSpiral bevel gearSimulationPhysics

Abstract

fetched live from OpenAlex

Planetary gearboxes are widely used in industrial applications, such as wind turbines, automobiles, and mining equipment. Although well designed, planetary gearboxes may experience failures if no early detection is conducted due to harsh working environment, such as high torque and heavy dust. To improve system reliability and reduce maintenance cost, it is important to carry out early fault detection. This study investigates the dynamic responses of a planetary gearbox with a tooth root crack in the sun gear. Reported studies have not considered the effects of bearing clearance on dynamic responses of planetary gearboxes with sun gear tooth crack. In this study, we consider the carrier bearing clearance, planet gear bearing clearance, and sun gear bearing clearance in a planetary gearbox with fixed ring gear. Simulation results reveal that if the bearing clearance is much smaller than the displacement of its corresponding gear, the clearance will not affect the dynamic response. Otherwise, the clearance will increase the displacement of its corresponding gear and change the dynamic responses of other gears.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
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 science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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