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Record W2136340500 · doi:10.1109/08ias.2008.22

A New Robust Method To Detect Rotor Faults in Salient-Pole Synchronous Machines Using Structural Asymmetries

2008· article· en· W2136340500 on OpenAlex
Prabhakar Neti, Ali Banitalebi Dehkordi, A.M. Gole

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSalientStatorRotor (electric)Control theory (sociology)Computer scienceFault (geology)DamperSIGNAL (programming language)Synchronous motorEngineeringControl engineeringArtificial intelligenceElectrical engineeringGeology

Abstract

fetched live from OpenAlex

This paper presents a novel method to detect rotor faults in salient-pole synchronous machines by exploiting small constructional asymmetries in the machine. In order to unambiguously detect the faults, it is very important to identify a signal that is minimally sensitive to the abnormal operating conditions of the machine but highly sensitive to the fault. This paper shows that certain frequency components such as the 30, 90 Hz in the stator current of a 4-pole 60 Hz salient-pole synchronous machine (SM) are good candidates for detecting rotor faults. The paper presents the physical explanation behind these observed frequency components and shows them to be suitable candidates to detect broken damper bars and inter-turn faults in the field winding.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.234
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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

Quick stats

Citations38
Published2008
Admission routes1
Has abstractyes

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