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Initial Condition Monitoring Experience on a Wind Turbine

2012· article· en· W3136377744 on OpenAlex
Eric Bechhoefer, Mathew Wadham-Gagnon, Bruno Boucher

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

VenueAnnual Conference of the PHM Society · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsCentre Intégré de Santé et Services Sociaux de la Gaspésie
Fundersnot available
KeywordsTurbineEnvironmental scienceWind powerCondition monitoringMarine engineeringComputer scienceEngineeringAerospace engineeringElectrical engineering

Abstract

fetched live from OpenAlex

The initial installation of a condition monitoring system (CMS) on a utility scale wind turbine produced a number of unexpected results. The CMS was installed on the TechnoCentre éolien Repower MM92. The installation allowed testing of a MEMS (microelecctromechanical system) based sensor technology and allowed and in-depth analysis of vibration data and revolutions per minute (RPM) data. A large 3/revolution effect, due to tower shadow and wind shear, required the development of an enhanced time synchronous average algorithm. The ability to easily measured changes in main rotor RPM, as a result of tower shadow and wind shear phenomenology, may also facilitate the detection of icing or blade pitch error.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.436

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 communication0.0000.000
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 head0.049
GPT teacher head0.334
Teacher spread0.285 · 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