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Record W2074668057 · doi:10.1115/detc2010-29126

EMD, Ranking Mutual Information and PCA Based Condition Monitoring

2010· article· en· W2074668057 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsMutual informationCondition monitoringRanking (information retrieval)Monotonic functionImpellerData miningComputer scienceArtificial intelligenceFault (geology)Pattern recognition (psychology)Feature (linguistics)Fault detection and isolationInformation fusionFeature extractionMachine learningEngineeringMathematics

Abstract

fetched live from OpenAlex

Success of any health monitoring system chiefly relies on the effectiveness of condition monitoring parameter. The parameter could be a single or combination of many vibration features. These features are expected to have a monotonic trend with the damage/fault progression. Ranking mutual information technique has the ability to detect the features that have monotonic trend and PCA is a popular and widely accepted multidimensional analysis tool for the feature fusion. A condition monitoring method is presented in this paper by combining EMD, ranking mutual information and PCA. The proposed method is helpful in generation of the indicator that represents the damage progression. This method is tested on the impeller health condition monitoring of a pump.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.264

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 head0.002
GPT teacher head0.183
Teacher spread0.180 · 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

Citations6
Published2010
Admission routes2
Has abstractyes

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