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Record W2163988724 · doi:10.1177/1475921709352144

A Resonance Demodulation Method Based on Harmonic Wavelet Transform for Rolling Bearing Fault Diagnosis

2009· article· en· W2163988724 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

VenueStructural Health Monitoring · 2009
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDemodulationFault (geology)HarmonicWaveletBearing (navigation)SIGNAL (programming language)Energy (signal processing)Filter (signal processing)Computer scienceFrequency bandAcousticsControl theory (sociology)Electronic engineeringHarmonic analysisEngineeringMathematicsArtificial intelligenceTelecommunicationsPhysicsBandwidth (computing)Channel (broadcasting)Computer vision

Abstract

fetched live from OpenAlex

Resonance demodulation technique is widely employed to diagnose faults of rolling bearings. In order to reduce the energy leakage influence of the traditional demodulated resonance method, a new approach based on harmonic wavelet transform (HWT) is proposed to extract the fault characteristics of rolling bearing. From the results of the numerical simulation analysis, this method is proven to be efficient in detecting the impact signal clouded in noises. Moreover, this article proposes a resonance demodulation scheme, which can obtain the optimal HWT parameters automatically to construct the proper sub-frequency band filter by calculating the relative wavelet energy of the different sub-frequency band. It solves the shortcoming in which a resonance frequency band filter is chosen manually. The proposed scheme is successfully applied to detect the fault of rolling bearings of a tilting mechanism in a converter mill.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
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
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.365
Teacher spread0.340 · 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