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Record W4400905511 · doi:10.1109/jestpe.2024.3432641

Online Diagnosis of Multiple-Switch Simultaneous Faults in SRM Drives Based on Current Errors

2024· article· en· W4400905511 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 Journal of Emerging and Selected Topics in Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Power Systems and Control
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCurrent (fluid)Computer scienceElectronic engineeringControl theory (sociology)Electrical engineeringEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This article presents a new online diagnostic technique for multiple power switch faults in switched reluctance motor (SRM) drives. Unlike the existing schemes, nearly all potential failure scenarios in the power switches of the standard asymmetric half-bridge (AHB) converter are considered in this work. First, a new current sensing scheme is proposed without modifying the standard AHB structure and the obtained current errors for current control of the drive system are analyzed for fault diagnosis. Second, a new fault variable is extracted from the dc-bus current, and subtests are introduced to detect and locate the failure of multiple switches in a phase-leg with the same or hybrid faults. Diagnosis of multiswitch faults in the same or different phases is achieved within a few sampling periods. The diagnostic capability of the proposed methods is thoroughly validated through experimental studies using a three-phase 12/8-pole SRM drive system.

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 score0.237
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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