The effects of the shape of localized defect in ball bearings on the vibration waveform
Bibliographic record
Abstract
Ball bearings, one of the most widely used components in rotating machinery, play a critical role in system performance. Localized defects such as pit and spall may develop in ball bearings during service. The vibration waveform of the impulse generated by a ball passing over a defect on the races is determined by the shape and size of the localized defect. Hence, it is important to study the relationship between the localized defect shape and its pulse waveform characteristics in order to diagnose the types of defect in bearings. This study examines the effects of the defect shape, radial load and shaft speed on the pulse waveform characteristics generated by localized defects using the method of explicit dynamic finite element analysis. To validate the proposed model, the results obtained from the experiments have also been provided, and the waveform and the duration of the pulse generated by the defect on the outer race are in good agreement with the simulation results, which shows validation of the proposed model. Both the experimental results and the simulation results have confirmed that the impulse shape generated by the defect on the raceway will be influenced by the contact deformations at the edges of the defect. The results obtained also demonstrate that the explicit dynamic finite element analysis approach can be used to analyze the pulse waveform characteristic generated by localized defects in ball 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 | 0.002 |
| 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".