A New Prognostic Approach for Hydro-generator Stator Windings
Why this work is in the frame
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Bibliographic record
Abstract

 
 
 Significant improvements in hydro-generator diagnostics were achieved, in the past decades, by using continuous online measurements and a number of periodic tests. In recent years, the diagnostic raw data has been converted into more useful information by way of integrated diagnostic systems that used expert knowledge. For example, an integrated methodology for hydro-generator diagnostics was developed at Hydro-Québec’ s research institute (IREQ) using a Web-based application. This comprehensive diagnostic system gives the degradation state of generator stator winding insulation by using a portfolio of diagnostic tools. Combining the results leads to a health index ranging from 1 (good condition) to 5 (worst condition). This system is used by Hydro-Québec’s power plant managers as well as technical support and maintenance engineers in the context of condition-based maintenance (CBM). The next step of development is to add new prognostic-related features. This involves automatic identification of active failure mechanisms, root cause analysis and estimation of the stage of advancement of any active mechanism. These characteristics form the basis of predictive maintenance and support the optimization of maintenance strategies.
 The approach is based on a number of causal trees (the failure mechanisms) formed by the combination of sequential physical degradation states that ultimately lead to a failure mode. Each combination of sequential physical states is unique and defines a particular failure mechanism. Failure mechanism analysis was followed by identification of all symptoms (diagnostics measurements, observations) with their respective thresholds defining each physical state.
 This paper presents the development of a prognostic approach where the modeling of failure mechanisms is combined with observable symptoms from our diagnostic system for the identification of active failure mechanisms.
 
 
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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) | 0.000 | 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 it