Virtual sensors to generate turbine runner blade strains from indirect measurements
Bibliographic record
Abstract
Abstract Strain measurements on turbine blades are difficult and costly tasks. Such measurements, when carried out, generally only happen during the runner commissioning. This gives rise to two problems. The first is that some of the sensors often stop functioning properly during the measurement campaign, which leads to distorted data, and the second is that runner blade strains are not available for long-term monitoring after the measurement campaign. To alleviate the consequences of distorted or missing values, we propose the use of neural networks to automate the imputations of missing values in measurement campaign data using virtual sensors. Three types of network architecture are proposed: Long Short-Term Memory (LSTM) in different multi-stage/multi-layer configurations in Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), injector multi-scale attention network (Injector MA-Net), and a combined architecture using both. The performance of these architectures will be compared in four situations: the loss of strain gauge rosette branches; the loss of a complete strain gauge rosette; the loss of data on a complete blade; and the absence of strain data, which is related to the problem of identifying which sensors could be used for long-term monitoring. The performance of the proposed algorithms will be evaluated on real case scenarios from a measurement campaign during a recent unit commissioning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".