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Record W2294840316

Qualification of Programmable Electronic System (PES) equipment based on international nuclear I and C standards

2006· article· en· W2294840316 on OpenAlexaboutno aff
John de Grosbois, G.A. Hepburn, R.A. Olmstead, William M. Goble, Vinod Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear powerReliability engineeringQuality assuranceEngineeringReliability (semiconductor)CertificationElectronicsNuclear fuel cycleProcess (computing)Systems engineeringSafety assuranceSafety standardsProduct (mathematics)Product certificationRisk analysis (engineering)Manufacturing engineeringComputer scienceElectrical engineeringPower (physics)Operations managementRadioactive waste
DOInot available

Abstract

fetched live from OpenAlex

Nuclear power plants (NPPs) are increasingly faced with the challenge of qualifying procured equipment, sub-components, and systems that contain digital programmed electronics for use in safety-related applications. Referred to as a 'programmable electronic system' (PES), such equipment typically contains both complex logic that is vulnerable to systematic design faults, and low voltage electronics hardware that is subject to random faults. Procured PES products or components are often only commercial grade, yet can offer reliable cost effective alternatives to custom-designed or nuclear qualified equipment, provided they can be shown to meet the quality assurance, functional safety, environmental, and reliability requirements of a particular application. The process of confirming this is referred to as application-specific product qualification (ASPQ) and can be challenging and costly. This paper provides an overview of an approach that has been developed at Atomic Energy Canada Limited (AECL) and successfully applied to PES equipment intended for use in domestic Candu R 6 nuclear power plants and special purpose reactors at Chalk River Laboratories. The approach has evolved over the past decade and has recently been adapted to be consistent with, and take advantage of new standards that are applicable to nuclear safety-related I and C systems. Also discussedmore » are how recognized third-party safety-certifications of PES equipment to International Electrotechnical Commission (IEC) standards, and the assessment methods employed, may be used to reduce ASPQ effort. (authors)« less

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.339
Teacher spread0.316 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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