A hydrogenerator model-based failure detection framework to support asset management
Bibliographic record
Abstract
Electrical utilities in North America significantly increased their installed capacities between 1960 and 1990. This ageing fleet is now forcing the producers to begin to use a holistic asset management in a more systematic way by introducing diagnostic and prognostic tools to support them in their decision-making process. For the last few decades, the Hydro-Quebec Research Institute has been working to understand ageing mechanisms and developing a diagnostic and prognostic causal graph model for hydrogenerators based on expert knowledge and diagnostic data. This paper proposes asset and asset system metrics based on graph theory to estimate the probability of detecting a failure using the number of detectable early warning signs. Proposed indicators intend to inform operators and decision makers on the failure detection probability for each individual asset and to identify critical failure detection of assets at an asset system level. An analysis has been carried out on a real hydropower plant for each of its sixteen hydrogenerators. Some results will be presented and critical failure detection rates for hydrogenerators will be identified. A framework will be proposed to improve asset management.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".