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Record W2058886196 · doi:10.1109/ieem.2009.5372903

Reliability prediction of an Ethernet Bus Interface Controller in a nuclear power plant simulator

2009· article· en· W2058886196 on OpenAlexaffabout
Lixuan Lu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Time Synchronization Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsEthernetReliability (semiconductor)UpgradeController (irrigation)Interface (matter)Nuclear power plantReliability engineeringEmbedded systemFailure rateLocal area networkComputer sciencePower (physics)EngineeringSimulationOperating system

Abstract

fetched live from OpenAlex

Obsolescence presents great challenge to nuclear power plants (NPP) and plant simulators around the world. Old designs will have to be modified or replaced by new designs, in order to simplify maintenance, increase availability and meet ever-increasing operational and training requirements. Control system upgrade and distributed control system (DCS) design for both old plants and new builds have become the center of interest. This paper describes a new Ethernet bus interface controller (eBIC) used in the input/output (I/O) system of a nuclear power plant (NPP) simulator in Canada. The reliability of the eBIC is evaluated, and compared with the reliability of the existing bus interface controller (BIC). It is shown that the predicted failure rate of the new eBIC is five times lower than that of the existing system. This raises the level of confidence in the new design. The reasons for this improvement are 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations2
Published2009
Admission routes2
Has abstractyes

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