MétaCan
Menu
Back to cohort
Record W1608040072 · doi:10.4271/2007-01-3878

Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management

2007· article· en· W1608040072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsImpact
FundersNaval Air Systems CommandAir Force Research LaboratoryU.S. Air ForceDefense Advanced Research Projects Agency
KeywordsGenerator (circuit theory)Computer scienceAutomotive engineeringEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">In cooperation with the major propulsion engine manufacturers, the authors are developing and demonstrating a unique very high frequency (VHF) vibration monitoring system that integrates various vibro-acoustic data with intelligent feature extraction and fault isolation algorithms to effectively assess engine gearbox and generator health. The system is capable of reporting on the early detection and progression of faults by utilizing piezoelectric, optical, and acoustic frequency measurements for improved, incipient anomaly detection. These gas turbine engine vibration monitoring technologies will address existing operation and maintenance goals for current military system and prognostics health management algorithms for advanced engines. These system features will be integrated in a state-of-the-art vibration monitoring system that will not only identify faults more confidently and at an earlier stage, but also enable the prediction of the time-to-failure or a degraded condition worthy of maintenance action.</div> <div class="htmlview paragraph">The authors have made significant progress toward identifying, computing, and comparing the high frequency feature sets generated with various vibro-acoustic measurement techniques. Specifically, the technology has been demonstrated on two subscale test stands. The first is a generator test rig that was equipped with a laser vibrometer and two high-frequency accelerometers. Various mechanical and electrical faults were seeded, with an emphasis on generator bearing faults. Initial results show very good detection capability in frequency bands well above those used in traditional vibration analysis. Another focus, accessory gearbox systems, was addressed for feasibility through a gearbox test rig, which was instrumented with high bandwidth accelerometers and wideband and narrowband acoustic emissions (AE) sensors. Baseline, seeded fault, and fault progression tests were conducted, including tests with various levels of gear tooth corrosion. Successful detection of this fault was then demonstrated using a number of new, innovative approaches. A statistical analysis was also performed to compare the approaches, with narrowband acoustic emission and high frequency vibration features performing the best.</div>

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.250
Teacher spread0.239 · 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