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Record W4246829677 · doi:10.1109/esw.2015.7094870

Enhanced safety features in motor control centers and drives for diagnostics and troubleshooting

2015· article· en· W4246829677 on OpenAlexaff
Larry R. Olsen, John Kay, Martin Van Krey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsRockwell Automation (Canada)
Fundersnot available
KeywordsTroubleshootingElectrical equipmentDoorsReliability engineeringElectrical shockAutomotive engineeringArc flashMaintenance engineeringElectric motorEngineeringPreventive maintenanceComputer scienceControl (management)Medical equipmentElectrical engineeringVoltageMechanical engineering

Abstract

fetched live from OpenAlex

It is common practice in industry for electrical workers to be exposed to shock, arc-blast and arc-flash hazards as they perform routine maintenance and diagnostic testing on equipment enclosed in motor control centers. This paper will illustrate the influence various IEEE conferences, including the Electrical Safety Workshop, has had on changing the electrical safety culture related to equipment diagnostics. End users are now requesting manufacturers of low and medium voltage motor control centers to develop or add features and functionality into their products that enhance the ability of electrical maintenance and operating personnel to conduct routine diagnostic and troubleshooting tasks without being directly exposed to energized electrical equipment. This paper will explore some of these features and provide some examples of routine maintenance and diagnostic tasks that can be performed while doing troubleshooting and diagnostic tasks on operating equipment without opening enclosure doors and exposing workers to the hazards associated with exposed energized equipment.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.222
Teacher spread0.215 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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