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Record W4411660756 · doi:10.1177/10775463251348767

Vibration characteristics of a double-row tapered roller bearing with raceway defects

2025· article· en· W4411660756 on OpenAlexaff
Jingyang Zheng, Jinchen Ji, Van-Canh Tong, Shan Yin, Shuai Zhang, Yuejian Chen

Bibliographic record

VenueJournal of Vibration and Control · 2025
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRacewayVibrationStructural engineeringBearing (navigation)Roller bearingMaterials scienceEngineeringAcousticsMechanical engineeringComputer sciencePhysicsFinite element methodLubrication

Abstract

fetched live from OpenAlex

Double-row tapered roller bearings (DTRBs) offer significant advantages, including high load-carrying capacity, long service life, and the ability to withstand combined radial and axial loads as well as bending moments. DTRBs are widely used in mechanical equipment and machinery, such as precision machine tools, automobiles, rail transit, and wind turbines. While previous studies have primarily focused on the static analysis of DTRBs, few have investigated their vibration mechanisms. To explore the dynamic characteristics of DTRBs, a comprehensive dynamic model was developed, incorporating local defects. This model reveals the vibration behavior of DTRBs with local defects and analyzes the impact of bearing defects on vibration characteristics. The findings provide a theoretical foundation for DTRB defect diagnosis and serve as a basis for future studies on the dynamics of various roller bearings.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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 designBench or experimental
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

Citations2
Published2025
Admission routes1
Has abstractyes

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