Influence of eccentric force and roller failure on vibration response of slewing bearing
Bibliographic record
Abstract
Slewing bearings are important components in heavy-duty machinery, with their failure mechanisms critically impacting operational reliability. Firstly, considering the coupling effect of roller defects and tilting of inner ring, a three-dimensional dynamics model is developed. And the nonlinear vibration equation with time-varying contact stiffness is constructed based on Hertz contact theory. Then the defect-roller dynamic collision process is characterized by the introduction of the defect roller motion trajectory function. Moreover, the fourth-order Runge–Kutta method is used to solve the vibration response of the system, emphasizing on analyzing the dynamic coupling mechanism of roller defect size, eccentric load angle, and contact force. Finally, a slewing bearing experiment platform is built to verify the simulation results. The changes in stiffness and intrinsic frequency of the slewing bearing are measured by the eccentric load under different defect sizes. Time-domain analysis reveals defect size-dependent amplitude characteristics, with a peak increase of 42.6% observed. Spectral analysis revealed distinct mf o ± nf r modulation patterns (fundamental frequency error ≤ 7.86%). The multiparameter coupling model proposed in this study reveals the mapping law of defect size-vibration response under eccentric load condition. It provides a theoretical basis for the fault diagnosis of slewing bearings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".