Uncertainties on modal parameters by operational modal analysis
Bibliographic record
Abstract
When operational modal analysis (OMA) is conducted on operating machines or structures without knowing excitations and perturbations neither the number of concerned frequencies, one problem raises on the validity of modal parameters identification (especially damping ratios) and on the precision of results obtained. This paper presents an OMA method allowing for the evaluation of uncertainties of modal parameters (frequencies, damping ratios and mode shapes). The method is based on the vector autoregressive model (VAR) for multiple numbers of measured channels. It is seen that the uncertainty of modal parameters decreases with higher model orders; the calculation of uncertainty allows also for the construction of an objective criterion for the selection of computing model order based on a threshold of confidence interval. The derivation confirms also that the identification of natural frequencies deals with a smaller uncertainty compared with the damping ratio estimation, and hence it can be conducted with a lower computing model order. Numerical simulations and experiments on a steel plate show the feasibility and the effectiveness of the developed OMA method.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".