Asset health index method for diesel generation unit replacement planning
Bibliographic record
Abstract
Diesel generation units are not often considered for replacement before they reach the manufacturer's recommended life, and sometimes only after it runs to failure. Although this can take better advantage of the unit life, it may not be the most cost effective strategy considering the fact that aging units often have higher operation and maintenance costs, and undesirable reliability consequences. In order to allow financial retain/replace decisions to be made; this paper proposes a cost effectiveness analysis method. Each generation unit under evaluation is given an asset health index (AHI), which is defined as the ratio of the NPV of running a unit to end of life over the NPV of immediate replacement. AHI is a self-explanatory quantitative indicator of the cost effectiveness for unit replacement. In-house software named asset health index calculator (HI) has been developed and an AHI has been produced for every diesel generation unit in service in the BC Hydro non-integrated areas (NIA) for the future 20 years, so planners can easily foresee when and which units should be replaced in this planning window. A performance evaluation of the generation units is also required by not discussed in this paper. The development of the AHI and the performance evaluation allow proactive fleet capacity planning and justifiable asset management expenditure. As an effective planning tool, the proposed AHI method can be extended to the evaluation for other aging assets.
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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 | 0.002 | 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 | 0.000 | 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".