Technico-economic modelling of maintenance cost for hydroelectric turbine runners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 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 | 0.001 |
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 it