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Record W2119710265 · doi:10.1115/detc2010-29232

Engine Oil Condition Monitoring Using High Temperature Integrated Ultrasonic Transducers

2010· article· en· W2119710265 on OpenAlexaff
Makiko Kobayashi, Zhensheng Sun, C. K. Jen, K.-T. Wu, Jeff Bird, Brian Galeote, Nezih Mrad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor Technologies Research
Canadian institutionsDepartment of National DefenceMcGill UniversityNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceUltrasonic sensorSump (aquarium)LubricantTransducerAcousticsPiezoelectricityCapacitive micromachined ultrasonic transducersComposite materialEngineering

Abstract

fetched live from OpenAlex

High temperature integrated ultrasonic transducers (IUTs) made of thick piezoelectric composite films have been coated directly onto the lubricant oil supply and sump lines of a modified CF700 turbojet engine. These thick piezoelectric films are fabricated using a sol-gel spray technology. The center frequencies of these IUTs are in the range of 10 to 12 MHz. The top electrodes, electrical wires, conducting adhesive bond, connectors and cables have been tested successfully for temperatures of up to 500°C. By operating these IUT in transmission mode, the amplitude and velocity of transmitted ultrasonic waves across the flow channel of the lubricant oil in supply and sump lines were measured during engine operation. The results have shown that the strength of the ultrasonic waves is sensitive to the presence of air bubbles in the oil and that the ultrasound velocity is linearly dependent on oil temperature. Based on the sensitivity of ultrasound velocity to oil temperature, a method for real-time monitoring of engine oil degradation is proposed.

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 score0.002

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 integrity0.0000.000
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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