Investigation of the dynamic local skidding behaviour of rollers in cylindrical roller bearings
Bibliographic record
Abstract
Skidding has a great influence on the reliability and the life of rolling element bearings. The previous research of bearing skidding mainly focussed on the gross skidding of bearings under severe conditions. However, the local skidding of rolling element also exists and cannot be ignored when bearing normally runs at steady state. In this paper, considering the discontinuous contact between the roller and the cage, the Hertz contact between the roller and races, lubrication, centrifugal effects and the cage pocket clearance, a nonlinear dynamic model is established to investigate the local skidding behaviour of a roller in cylindrical roller bearings. The equations of the dynamic model are solved by the fourth-order Runge–Kutta algorithm and verified by comparing simulation results with the published experimental data. The results show that the local skidding of the roller still noticeably exists in bearings even when the gross skidding can be ignored, especially on the inner raceway. The local skidding at the entry of the loaded zone may cause the smearing on the raceway surface. However, it can be found that the probability of the local skidding of the roller will be decreased by increasing radial load and reducing the bearing rotational speed. The cage pocket clearance would induce violent fluctuations on the roller rotational angular velocity as well as on the sliding velocity. Furthermore, it can generate extraordinary impact forces between the rollers and the cage. Thus, the cage pocket clearance should be carefully considered in the design of bearings.
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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 | 0.000 | 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 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".