Power Plant Life/Risk Assessment Techniques
Bibliographic record
Abstract
Many fossil power plants in N. America are now required to operate in excess of 20 years beyond their original design life. To ensure safe reliable operation, life assessment of key high-risk components is required. Life assessment of older power plant requires the application of many diverse techniques and the integration of several engineering disciplines with plant operations experience. Traditional approaches to life assessment of high temperature components will be described and illustrated with examples taken from experience on large coal fired plants from the old Ontario Hydro system. These results will include a number of high energy piping welds with approximately 160,000 hours of service that contain intercritical region heat affected zone (HAZ) creep damage (Type IV). These welds have been monitored through in-situ metallography techniques for over 20,000 hours since damage was first detected. New techniques are also being developed or employed: miniature sample testing for evaluating creep and fracture properties along with miniature EDM sample removal techniques. The use of innovative mechanical scratch gauges for the purpose on-line strain monitoring of mechanical or civil structures. The use of orientation imaging microscopy (OIM) for simultaneous comprehensive characterization of the interaction between residual plastic strains, texture, grains size distribution and other crystallographic features as applied to root cause failure analysis. Finally risk based asset management software RBMS for the purpose of detailed component assessments. Examples/results of these techniques will also be presented in the paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.013 | 0.004 |
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".