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Record W4392499070 · doi:10.1109/jsen.2024.3370841

A Fiber-Only Optical Vibration Sensor Using Off-Centered Fiber Bragg Gratings

2024· article· en· W4392499070 on OpenAlexaff
Ariana Mahlooji, Fae Azhari

Bibliographic record

VenueIEEE Sensors Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFiber Bragg gratingPHOSFOSMaterials scienceFiber optic sensorOptical fiberPlastic optical fiberPolarization-maintaining optical fiberPhotonic-crystal fiberGraded-index fiberFiberOpticsOptoelectronicsVibrationAcousticsPhysicsComposite material

Abstract

fetched live from OpenAlex

Vibration monitoring of rotating machinery is crucial for operational safety and optimized maintenance. In this work, we propose a fiber Bragg grating (FBG) vibration sensor that does not involve any other components beyond the optical fiber on which the FBG is inscribed. The optical fiber is supported at one end to form a cantilever arrangement. The FBG is positioned at the support, intentionally offset from the center of the fiber core. Continual bending of the cantilever beam due to vibration induces axial tensile and compressive stresses away from the neutral axis, where the off-centered FBG is located. This causes a reciprocating shift in the FBG’s central wavelength, enabling us to measure the frequency and amplitude of the applied vibration. Since central wavelength is the measurand, the proposed sensor is compatible with wavelength division multiplexing, thereby expanding its utility across diverse industries. The sensor prototype fabricated and tested in this study, performed linearly within a wide range of frequencies (20–1500 Hz), and was responsive to accelerations as low as 0.3 g. The sensor characteristics can be tailored to a specific application by fine-tuning the dimensions and support configurations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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