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Record W2513528654 · doi:10.1109/tim.2016.2598019

A Morphological Hilbert-Huang Transform Technique for Bearing Fault Detection

2016· article· en· W2513528654 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2016
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBearing (navigation)DemodulationFault detection and isolationFault (geology)Hilbert transformVibrationRolling-element bearingLinearitySIGNAL (programming language)Signature (topology)Feature (linguistics)Computer scienceFilter (signal processing)Control theory (sociology)EngineeringElectronic engineeringSignal processingPattern recognition (psychology)AcousticsArtificial intelligenceMathematicsComputer visionElectrical engineeringPhysicsDigital signal processing

Abstract

fetched live from OpenAlex

Most rotary machinery imperfections are related to defects in rolling element bearings. Unfortunately, reliable bearing fault detection still remains a challenging task, especially when bearing defect-related features are nonstationary. A new morphological Hilbert-Huang (MH) technique is proposed in this paper for incipient bearing fault detection. In the proposed MH technique, a new linearity measure method is suggested to demodulate characteristic feature functions, and a mathematical morphological filter is proposed to reduce impedance effect of the measured vibration signal to improve fault detection accuracy. The effectiveness of the proposed MH technique is verified by a series of experimental tests corresponding to different bearing conditions.

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: none
Teacher disagreement score0.957
Threshold uncertainty score0.573

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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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