A Numerical Model to Predict Damaged Bearing Vibrations
Bibliographic record
Abstract
This work aims to develop the theoretical fundamentals and numerical details of new software, dedicated to the simulation of the dynamic behavior of rotating ball bearings in the presence of localized surface defects. In this article, the generation of vibration by a point defect in a rolling element bearing is modeled as a function of the rotation of the bearing, of the distribution of the load in the bearing, of the bearing structure elasticity, of the oil film characteristics, and of the transfer path between the bearing and the transducer. The numerical model is developed with the assumption that the dynamic behavior of the bearing can be represented by a coupled three-degree-of-freedom system, after which the governing equations of the simulation model are solved using computer simulation techniques. A new application, called BEAT (BEAring Toolbox), was developed in order to simulate bearings' vibratory response to the excitations produced by localized defects. By adding a noisy response due to the sliding friction occurring between the moving parts to the impulsive response caused by localized defects, the BEAT software is able to provide realistic results, similar to those produced by a sensor during experimental measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".