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Record W2165683298 · doi:10.1109/tec.2006.882417

Real-Time Implementation of Wavelet Packet Transform-Based Diagnosis and Protection of Three-Phase Induction Motors

2007· article· en· W2165683298 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

VenueIEEE Transactions on Energy Conversion · 2007
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInduction motorWavelet packet decompositionWavelet transformWaveletFault detection and isolationComputer scienceFault (geology)Control theory (sociology)EngineeringAlgorithmArtificial intelligenceVoltageElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a real-time implementation of an online protection technique for induction motor fault detection and diagnosis. The protection system utilizes a wavelet packet transform (WPT)-based algorithm for detecting and diagnosing various disturbances occurring in three-phase induction motors. The criterion for the detection is the comparison of the coefficients of the WPT of line currents using a mother wavelet at the second level of resolution with a threshold determined experimentally during the healthy condition of the motor. The algorithm is implemented in real-time using the Texas Instrument TMS320C31 32-b floating-point digital signal processor with the help of object-oriented programming. The proposed technique is tested on two three-phase induction motors. The online test results give a response signal at the instant or within one cycle of disturbance in all cases of investigated faults. In addition, the algorithm is also tested during no load and full load operating conditions of the motor.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.821

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 head0.273
Teacher spread0.261 · 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