Technico-economic modelling of maintenance cost for hydroelectric turbine runners
Bibliographic record
Abstract
Large utilities need to optimize the investment made to maintain their assets. For a utility like Hydro-Québec (37 GW) an important part of those investments are made to maintain their hydroelectric facilities. To minimize the maintenance cost, technico-economic model enabling the propagation of uncertainty associated with the degradation processes of a given component seems essential. Therefore, for Francis hydroelectric turbine runners, we developed two technico-economic models: one for crack propagation and one for cavitation. Since these are the main degradation mechanisms leading to failure of Francis runners, they enable us to study the effect of maintenance strategies on the maintenance cost of these components. The model has been created using VME, an asset management software developed by EDF R&D (Électricité de France). VME uses Monte-Carlo simulations to generate stochastic failure dates and obtains probabilistic indicators of the net present value of a given management strategy. We will use a study case based on a Hydro-Québec (Québec, Canada) facility to illustrate the importance of the proper assessment of current and expected long-term reliability on maintenance cost. The paper will be structured as follows. First, an overview of the modelling strategy will be presented. Then, we will have a closer look on how VME, the tool used for Monte-Carlo simulations, derive its results. Finally, we will present a study case and discuss the results obtained in terms of the sensitivity to the reliability assessment uncertainties.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".