Quantification of Visual Inspection Results to be Integrated as a Diagnostic Tool for Hydrogenerators
Bibliographic record
Abstract
Visual inspection of generators is a widely used and industry-proven diagnostic technique. It can detect or confirm many symptoms or anomalies related to the generator failure mechanisms and complements conventional diagnostic tools to provide a more comprehensive assessment of the condition of generators. However, a visual inspection tool poses two major challenges: quantifying the symptoms of observed degradation and managing the information. Over the past decade, Hydro-Quebec has developed a methodology for quantifying the observations made during visual inspection in the same way as any other diagnostic tools. The initial version of the visual inspection tool only included the stator components. Quantification algorithms from this first version were updated based on feedback from more than ten years of collecting stator visual inspection results, and rotor components were integrated into the diagnostic tool. The latest version of the visual inspection tool for stator and rotor has been implemented in a new in-house application called DIAAA. The aim of this paper is to present the methodology developed to objectively quantify the results of visual inspection of stator and rotor components. The structure is described for the automatic calculation of the condition index based on the classification of observable symptoms, subcomponents, and components. Results obtained from the visual inspection tool can now be judiciously combined with other tools to improve the diagnostic of generators and automatically identify stator and rotor failure mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".