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Record W2387352252 · doi:10.1177/0309524x16647842

Condition monitoring and fault diagnosis of a small permanent magnet generator

2016· article· en· W2387352252 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

VenueWind Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVibrationCondition monitoringWind powerFault (geology)WaveletTurbineRotor (electric)Permanent magnet synchronous generatorEngineeringFault detection and isolationMagnetAutomotive engineeringWavelet transformContinuous wavelet transformComputer scienceDiscrete wavelet transformAcousticsMechanical engineeringElectrical engineeringActuator

Abstract

fetched live from OpenAlex

Small wind turbines are often used in remote locations, making them difficult and expensive to repair. This suggests the need for remote condition monitoring and fault diagnosis which has not been used extensively for small turbines. In order to investigate small direct drive wind turbine generators working at variable speed and develop new condition monitoring and fault diagnosis techniques, a test facility based on a 500 W permanent magnet generator was built. The investigation concentrated on mechanical faults which include ball bearing outer race defect and a rotor imbalance. Electrical load imbalance was also investigated. Imbalance detection was performed through wavelet power spectrum analysis of vibration signals. Three time–frequency analysis techniques were performed on vibration signals and performances are compared. These were short time Fourier transforms, continuous wavelet transform, and order analysis. Order analysis proved to be a simple, intuitive, and reliable technique for vibration analysis under variable speed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.660

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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