Qualification of Programmable Electronic System (PES) equipment based on international nuclear I and C standards
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".