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Record W1966293933 · doi:10.1115/imece2010-39851

Diagnosis of Defected Rolling Bearings via Vibration Analysis Method

2010· article· en· W1966293933 on OpenAlexaff
Majid Hamedynia, Hossein Rokni D. T.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsVibrationBearing (navigation)Ball bearingStructural engineeringRolling-element bearingBall (mathematics)Frequency domainCondition monitoringEngineeringComputer scienceMechanical engineeringLubricationAcousticsArtificial intelligence

Abstract

fetched live from OpenAlex

The main function of rolling element bearings is to provide low friction conditions for supporting and guiding a rotating shaft. The rolling element bearing includes both ball bearings and roller bearings. Rolling element bearings operate with a rolling action whereas plain bearings operate with a sliding action. In various applications, these bearings are considered as critical mechanical components since defect in these components may lead to malfunction and catastrophic failure in some cases. Vibration analysis is one of the most established methods used to evaluate the condition of bearings in operating machines. In this paper, an abnormal detection structure, in which different types of abnormal detection routines can be applied, is proposed. Bearing fault modes and their effects on the bearing vibration are discussed. In order to achieve this purpose, a feature extraction method is developed to overcome the limitation of time domain features. Experimental data from bearings under different operating conditions are used to verify the proposed method.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.346
Threshold uncertainty score0.679

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)0.0010.000

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 head0.005
GPT teacher head0.273
Teacher spread0.268 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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