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Vibroacoustic Signal-Based Diagnosis for Rotor Faults in Large Synchronous Machines - an Overview

2023· article· en· W4385756079 on OpenAlexafffund
Rony Ibrahim, Antoine Tahan, Bachir Kedjar, Kamal Al‐Haddad, Ryad Zemouri, Arezki Merkhouf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsHydro-QuébecÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsRotor (electric)VibrationSIGNAL (programming language)Generator (circuit theory)EngineeringComputer scienceBar (unit)Fault (geology)Control engineeringElectronic engineeringPower (physics)Electrical engineeringAcoustics

Abstract

fetched live from OpenAlex

The objective of this paper is to present an overview of how vibroacoustic signals can be used to diagnose rotor faults in synchronous generator, specifically focusing on large hydrogenerators commonly used in power plants. The paper showcases an investigation highlighting the effectiveness of vibration and acoustics measurement in detecting common defects found in these machines, such as eccentricities, inter-turn short-circuits, and rotor broken bar faults. To accomplish this, the paper conducts an extensive literature review, comparing the advantages and disadvantages of various techniques used in diagnosing these faults.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
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 score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.330
Teacher spread0.304 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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