Predicting Future Asset Condition Based on Current Health Index and Maintenance Level
Bibliographic record
Abstract
With restructuring of the electricity sector into profit oriented business models, an increasing number of electric utilities are adopting health indices to measure and monitor the condition of their assets. The health indices represent a novel way for capturing and quantifying the results of operating observations, field inspections and in-situ and laboratory testing into an objective and quantitative picture, providing the overall health of the assets. Asset health indices become a powerful tool in managing assets and identifying investment needs and prioritizing investments into capital and maintenance programs. When appropriately developed, health indices provide an accurate indication of the probability of asset failures and associated risks. Having established the asset health index under current conditions, health index values in future can be predicted by taking into account the impact of environmental and operating conditions along with the preventative maintenance practices. This paper describes the techniques to account for impact of preventative maintenance on health indices and for predicting future asset condition based on the current health index and maintenance practices. The techniques can be used for evaluating future risks associated with an asset or in selecting optimal maintenance levels that would provide the right balance between risk and investment costs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".