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Record W2750424462 · doi:10.1108/jqme-06-2016-0028

Bearing temperature monitoring of a Wind Turbine using physics-based model

2017· article· en· W2750424462 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

VenueJournal of Quality in Maintenance Engineering · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSCADABearing (navigation)TurbineControl chartALARMWind powerEngineeringWind speedComputer scienceReliability engineeringControl engineeringMarine engineeringMechanical engineeringMeteorologyArtificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose a method to monitor a Wind Turbine’s (WT) main bearing, based on the difference between the temperature as measured by the Supervisory Control and Data Acquisition system (SCADA). Design/methodology/approach The monitoring of the main bearing is based on the difference between the measured temperature and the estimated temperature obtained from a dynamic model. The model used is based on the law of energy conservation. Several validation metrics have suggested that this model is accurate. Findings The Exponentially Weighted Moving Average control chart for two cases studies is used for the monitoring for the main bearing; this method has shown great potential for industrial applications. A failure was detected three weeks before the current actual alarm settings used by SCADA were able to identify the issue. Originality/value The proposed method is a monitoring method that can be used on most industrial wind farms and provide important information on the condition of the WTs’ main bearing.

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)0.004
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.289
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.184
GPT teacher head0.455
Teacher spread0.271 · 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