Bruce Power Aging Obsolescence Program
Bibliographic record
Abstract
The Aging and Obsolescence Program (AOP) developed by AECL in cooperation with Bruce Power ensures that Bruce Power is able to proactively manage plant critical component vulnerabilities before failure, thereby improving unit forced outage rates and reliability. AOP involves application of INPO AP-913 guidance for aging and obsolescence. The process includes component criticality identification and prioritization, single point vulnerability identification, and development of the technical basis to support maintenance. This includes replace or repair strategies, identification of critical spare parts and stocking parameters, through to the identification and resolution of obsolescence issues. The program is applicable to all components in a plant, but is being applied initially to critical components. This includes all Bruce B criticality category 1 components and those components that have caused plant trips or outages The program was also expanded to include review of buried piping for both Bruce A and B, additional plant systems based upon health status, and heat exchangers. Critical Spare Parts and Obsolescence Assessments identify and manage critical spare parts and obsolescence for Bruce Power SSCs (structures, systems, and components) and optimize spare parts inventory, which is intended to meet both planned and un-planned demand. In order to facilitate implementation of the AOP, the project includes integration of the program into Bruce Power processes and procedures. The cooperation between Bruce Power and AECL on AOP began as a small pilot project in 2007 where the program and procedures were developed by analysis of a few select components. The paper describes the Bruce Power Aging Obsolescence Program, the novelties and improvements of this integrated methodology and progress made to date.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".