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Record W4226169877 · doi:10.1109/tia.2022.3159628

Potential Ignition Sources and Protections in Electric Rotating Machines Operating in Explosive Gas Atmospheres

2022· article· en· W4226169877 on OpenAlexaff
Bharat Mistry, Rajendra Mistry, William G. Lawrence, Amnit Dhindsa

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsIgnition systemExplosive materialHazardous wasteProcess (computing)Gas compressorComputer sciencePrime (order theory)CriticalityElectrical equipmentAutomotive engineeringEnvironmental scienceRisk analysis (engineering)Reliability engineeringMechanical engineeringEngineeringBusinessWaste managementPhysicsAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

Electric rotating machines are the main prime movers to drive the necessary process equipment in the petroleum and chemical industries. Safety is a prime concern for personnel and processes. A breakdown can cause a loss of personnel and or revenue. As technology evolves, engineers continue to improve the design of these machines to be as safe as possible for the area in which they are selected for installation while also considering process criticality. This article will discuss the different potential ignition sources in electric rotating machines and how they are designed, manufactured and protected to mitigate the risk of fire/explosion. The applicable regulations/standards are discussed for the various protection techniques adopted for each hazardous area classification. A brief overview of installation practices is also discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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 designBench or experimental
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

Citations1
Published2022
Admission routes1
Has abstractyes

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