Incorporating Multi-Year Asset Replacement Time Into Calculation of Asset's Expected Annual Unavailability Due to End-of-Life Failure
Bibliographic record
Abstract
Aging asset management requires careful consideration of end-of-life of any asset. This is particularly serious with aging infrastructure which may require complete replacement of a large asset which has a long lead time. In many current End-of-Life analysis approaches to calculate unavailability, the period of interest is sub-divided into yearly cycles with the assumption that the asset is available at the start of the year. Although this assumption is adequate if the replacement time for the asset occurs within the year, with longer lead times this can create excessively optimistic availabilities. This paper presents an improved probabilistic tool for risk assessment due to End-of-Life failure unavailability which overcomes this deficiency. The End-of-Life methodology proposed in this paper is required when aging assets have long lead times to replacement and so the capital investment decision must be made several years ahead. The method is applied to evaluate the End-of-Life unavailability of Manitoba Hydro Bipole II HVdc converters. The results are corroborated using Monte Carlo simulation. Finally, the risk of End-of-Life is incorporated into an economic cost-benefit analysis corresponding to the presented unavailability evaluation method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".