Design of a vertical axis rotating machine test-bench and numericalmodelling
Bibliographic record
Abstract
Hydroelectricity is a widely exploited resource in North America where it represents 92% and 60% of the electricity produced in Quebec and Canada, respectively. Moreover, the current hydroelectric units are getting old and are evermore solicited, which increases the risks of sudden failures. In this study we developed a vertical axis rotating machinery aiming to improve the research models used for diagnosis and predictions of failures in hydro machineries. Horizontal vibrating shafts setups are already commercially available for academic activities but the dynamics they portray is very different from what is to be expected from a vertical-axis rotating machine (VARM) like the ones we find in hydro-electric generation units, thus the need to build a vertically rotating shaft test-bench. The VARM is firstly designed in a computer-aided design (CAD)/finite-element analysis (FEA) software such as Simcenter 3D to estimate its principal characteristics, like its critical speeds. FEA also allows us to predict the VARM response to certain loads which include unbalances and misalignments which are used to simulate failures in a vertically rotating system. The machine is monitored using two accelerometers, one on each bearing, two perpendicular laser micro-meters and two perpendicular high-speed cameras. The data is then processed, and bearing characteristics are estimated using system identification techniques. Finally, a numerical model of the VARM is solved using these bearing estimates and its response is compared to that of the VARM obtained with the high-speed cameras.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".