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Record W2981022241 · doi:10.4236/ica.2019.104009

An Evolving Fuzzy Classifier for Induction Motor Health Condition Monitoring

2019· article· en· W2981022241 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

VenueIntelligent Control and Automation · 2019
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsLakehead University
Fundersnot available
KeywordsInduction motorClassifier (UML)Fuzzy logicCondition monitoringComputer scienceArtificial intelligenceEngineeringTransparency (behavior)Control engineeringMachine learningData miningComputer security

Abstract

fetched live from OpenAlex

Induction motor (IM) is commonly used in various industrial applications. Reliable online IM health condition monitoring systems are critically needed in industries to improve operational accuracy and safety of the IMs and the machinery. A new evolving algorithm is proposed to provide more decision-making transparency, as well as better classification and processing efficiency. The effectiveness of the developed intelligent classifier is examined by simulation and experimental tests.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.513

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 head0.012
GPT teacher head0.301
Teacher spread0.289 · 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