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Record W3199702169 · doi:10.1049/rpg2.12281

Comprehensive aging assessment of pitch systems combining SCADA and failure data

2021· article· en· W3199702169 on OpenAlex
Lu Wei, Zheng Qian, Hamidreza Zareipour, Fanghong Zhang

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

VenueIET Renewable Power Generation · 2021
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Calgary
FundersProgram for Changjiang Scholars and Innovative Research Team in University
KeywordsSCADAReliability engineeringComputer scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Due to the recent rapid development of the wind energy industry, many wind turbines that have been operational over long periods will face degradation caused by aging effects; hence, an appropriate aging assessment method for wind turbines and their components is essential for optimizing the asset management and maintenance strategy of a wind farm. An aging assessment method is proposed for wind‐turbine electric‐pitch systems by introducing four individual aging indicators based on the examination of supervisory control and data acquisition (SCADA) and failure data, i.e. function, energy‐consumption, temperature, and reliability indicators. To obtain a reliable comprehensive assessment, an information‐fusion method has been developed based on a given reference value; the weighting factors in the information fusion were calculated based on the reference value, while reliability and robustness were verified using the SCADA and failure data from a wind farm over three years.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.669

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 head0.031
GPT teacher head0.309
Teacher spread0.278 · 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