Efficient analytical method to obtain the responses of a gear model with stochastic load and stochastic friction
Bibliographic record
Abstract
The friction, which is widely existed in practical, is seldom considered when modelling the gear system with stochastic load. Due to the variation of the temperature and lubrication condition, friction is a stochastic factor to a gear model. In this paper, a gear model with stochastic load, stochastic friction, and some other deterministic factors is considered. Due to the effects of stochastic factors (i.e., load and friction), the gear system faces more vibration and noise than the case with all deterministic factors. Thus, to analyse the variation of responses in the gear dynamical model, the corresponding dynamic equation needs to be solved. However, the statistic characteristics of dynamic responses are hard to obtain by numerical methods. Thus, an efficient analytical method is proposed, and then, an approximate analytical solution of the dynamic equation can be obtained in this paper. By the obtained solution, the vibration and noise of gear systems can be well investigated. Simulation results are provided to demonstrate the superior performance of the proposed 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".