Developments in an acoustic resonance test for the detection of manufacturing anomalies in hydroelectric generator stator windings
Bibliographic record
Abstract
To meet the ever-increasing demand for electricity, Hydro-Québec (HQ) is seeking to simultaneously increase the power of its generating stations while improving its service quality. Thus, the company has tasked its research institute to investigate innovate methods to meet the aforementioned goals. Of interest in the current study is the development of an acoustic resonance test (ART) to improve the quality control (QC) in the manufacturing of hydroelectric generator stator windings. Since HQ and its suppliers are investigating new fabrication methods for stator windings to meet the required timelines and increased power requirements, QC is required to ensure the service quality of its new hydroelectric generators. Typical manufacturing anomalies found in stator windings are delamination and air pockets between insulation layers. Such anomalies can result in an acceleration in the degradation of the winding insulation, which reduces the service quality of hydroelectric generators. To benchmark the ART method, the results of the suspected locations of the anomalies along the stator windings were compared with an acoustic camera and the locations were dissected and inspected under microscope for validation. Ten different stator windings were tested and two metrics (variations in both force and frequency responses) were found to indicate the location of delamination sites in the stator windings and corroborated with the result of both the acoustic camera and the dissections.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".