ICONE11-36262 Initiating Stochastic Maintenance Optimization at Candu Power Plants
Bibliographic record
Abstract
As previously reported at ICONE 6 in New Orleans (1996), the use of various innovative maintenance optimization techniques at Bruce has lead to cost effective preventive maintenance applications for complex systems. Further cost refinement of the station maintenance strategy is being evaluated via the applicability of statistical analysis of historical failure data. Since the statistical evaluation was initiated in 1999 significant progress has been made in demonstrating the viability of stochastic methods in Candu maintenance. Some of the relevant results were presented at ICONE 10 in Washington DC (2002). Success with the graphical displays and the relatively easy to implement stochastic computer programs was sufficient to move the program along to the next significant phase. This next phase consists of investigating the validity of using subjective elicitation techniques to obtain component lifetime distributions. This technique provides access to the elusive failure statistics, the lack of which is often referred to in the literature as the principle impediment preventing the use of stochastic methods in large industry. At the same time the technique allows very valuable information to be captured from the fast retiring "baby boom generation". Initial indications have been quite positive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".