Dependence analysis of planetary gearbox vibration marginals
Bibliographic record
Abstract
Time-frequency distribution (TFD) methods have been widely used for planetary gearbox fault detection. The aim of TFD is to represent a signal by a joint energy distribution in the time-frequency domain. Positivity is one of the most important properties for TFDs. Copula-based positive TFD construction methods utilize the time marginal, the frequency marginal and the dependence structure between the marginals. In this study, the dependence between the time marginal and the frequency marginal is studied explicitly by numerical and graphical rank-based statistics. Rank-based statistics are invariant with monotone transformations of the marginal distributions. This study demonstrates that the dependence does exist between the time marginal and the frequency marginal. The findings build one theoretical foundation for further study on copula-based TFD construction for one planetary gearbox vibration. Moreover, the results show that with the increase of the gearbox degradation, the Kendall's Tau's absolute value increases as well. This indicates that the more severe the fault is, the stronger the dependence would be.
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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) | 0.001 | 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 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".