Time series modeling of vibration signals from a gearbox under varying speed and load condition
Bibliographic record
Abstract
Accurate modeling of the baseline vibration signals generated from a healthy gearbox is critical to the success of time series model-based condition monitoring approach (TSMBA). Gearboxes often operate under varying rotation speed and load conditions, which makes the vibration signals non-stationary. It is challenging to accurately model such signals. Existing auto-regression models with exogenous variables (ARX) cannot model the time-varying spectral contents properly due to the limitation on its model structure. Aiming at improving the modeling accuracy, this paper proposes a functional series - operating condition dependent auto-regression (FS-OCAR) model. Legendre polynomials are used to describe the dependence between the operating condition and auto-regression parameters. FS-OCAR is validated using simulation signals from a fixed-shaft gearbox. The modeling accuracy is measured by calculating goodness-of-fit metric and mean squared error of the modeling residuals. Comparisons show that the proposed FS-OCAR outperforms the ARX.
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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".