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Record W2171370386 · doi:10.1177/107754630100700101

Traveling-Wave Modal Identification Based on Forced or Self-Excited Resonance for Rotating Discs

2001· article· en· W2171370386 on OpenAlexaff
Jifang Tian, Stanley G. Hutton

Bibliographic record

VenueJournal of Vibration and Control · 2001
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsModalExcitationExcited stateDisplacement (psychology)Resonance (particle physics)Modal testingAcousticsIdentification (biology)PhysicsModal analysisControl theory (sociology)Energy (signal processing)VibrationComputer scienceMaterials scienceAtomic physicsArtificial intelligence

Abstract

fetched live from OpenAlex

Modal testing of rotating systems, such as a rotating disc interacting with a stationary restraint, is considered in this paper. A practical and effective identification method called the artificial damping method (ADM) is proposed to identify the traveling-wave modes for rotating discs based on forced or self-excited resonant responses measured using two displacement probes. The ADM procedure can handle extremely noisy systems and does not require excitation information. The method is suitable for the detection of self- excited modes that may occur during operation. Additionally, this procedure can be effectively used to identify modal parameters for large-scale structures and other rotating systems with a relatively low level of excitation energy

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.273

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2001
Admission routes1
Has abstractyes

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