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Record W2329383950 · doi:10.1115/wtc2005-64282

A Comparative Study of Air-Coupled Ultrasound Sensor and Accelerometer in Detecting Bearing Defects

2005· article· en· W2329383950 on OpenAlexaff
Azzedine Dadouche, M. S. Safizadeh, Jeff Bird, Waldemar Dmochowski, David S. Forsyth

Bibliographic record

VenueWorld Tribology Congress III, Volume 2 · 2005
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAccelerometerAcousticsBearing (navigation)UltrasoundVibrationCondition monitoringUltrasonic sensorComputer scienceMaterials scienceElectronic engineeringEngineeringElectrical engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

The aim of this preliminary study is to investigate the capabilities of a non-destructive evaluation (NDE) sensor in detecting bearing faults. An air-coupled ultrasound sensor is used for this purpose. An accelerometer, which is the standard sensor used in industry, is also used in order to compare the output spectrums of the two sensor signals. A defect was created intentionally on the bearing components to simulate a fatigue crack or other relevant defect. The power spectra of vibration signals measured by accelerometer and ultrasound sensor are compared and their advantages and disadvantages are determined.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.301
Teacher spread0.275 · 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 designObservational
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

Citations5
Published2005
Admission routes1
Has abstractyes

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