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Record W4388225960 · doi:10.1080/10589759.2023.2274000

Fatigue crack localisation based on empirical mode decomposition and pre-selected entropy

2023· article· en· W4388225960 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

VenueNondestructive Testing And Evaluation · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of ManitobaPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilMinistry of Education
KeywordsVibrationStructural engineeringEntropy (arrow of time)LinearityHilbert–Huang transformMaterials scienceRobustness (evolution)AcousticsComputer scienceMechanicsMathematicsEngineeringPhysicsElectronic engineeringStatisticsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Fatigue cracks, especially at their initial stage, can lead to a repetitive crack open-close breathing-like phenomenon in the vibration response of structural elements. As such, regularities, bi-linearity, or perturbations in the vibration response can arise. Entropy can be used to quantify the irregularity or bi-linearity in these responses since there is an apparent variation of entropy values on the two sides of a breathing crack. Here, we present a new breathing crack localisation method based on a spatially distributed entropy approach coupled with the empirical mode decomposition technique. To enhance the robustness, a pre-selection mechanism is proposed to select the most suitable entropy method. The proposed method is then employed to localise the breathing crack in a beam in a laboratory setup. It is concluded that the proposed approach can be effectively used for breathing crack localisation in a structural element.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.101
GPT teacher head0.436
Teacher spread0.335 · 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