Bayesian-based system identification of a tilting pad journal bearingunder colored excitation
Bibliographic record
Abstract
We introduce an output-only Bayesian identification scheme to infer a bearing's restoring forces under colored excitation in a hydroelectric generation unit.Numerical modelling of hydroelectric generating units has been a common challenge in engineering for years, because of the complexity and the diversity of the physics involved.Indeed, many physics coexist in a single system: electrical, magnetic, mechanical, fluid and fluid-structure interactions.One common way to model units is done through the insight of rotordynamics.In such a paradigm, a hydroelectric unit is seen as a rotating rotor whose subcomponents are modelled independently, then assembled in a finite element model.When comparing models to actual data, one major difficulty is to separate the contribution of each of the subcomponents.Indeed, studying the behavior of a substructure requires the knowledge of acting forces, which depend upon the rest of the system's characteristics.Here we leverage Bayesian inference to develop a system identification method based on physic-based models with the purpose of contributing to add physics into data-oriented digital twins.Our aim is to recover design parameters from actual observations of the physical asset.The result will ultimately be embedded in a digital twin for monitoring, diagnosis, and prognosis purposes.For validation, we use a linearized model for bearing dynamics to simulate validation data under different types of excitations.Ground-truth parameters are chosen according to the short hydrodynamic bearing theory.The unscented Kalman filter is combined with a Markov chain Monte Carlo algorithm and deployed to recover linear stiffness and damping coefficients.Since we assume a linear dynamic, a Gibbs sampling is implemented to estimate the excitation applied on the bearing.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".