Dynamic simulation of a cylindrical roller bearing with a local defect by combining finite element and lumped parameter models
Bibliographic record
Abstract
Abstract Rolling element bearings are one of the key components of many rotating machines. Through condition monitoring and vibration analysis of bearings, valuable information about the health status of the machine can be obtained, such as detection of a local fault, unbalance, and misalignment. For more than three decades, various bearing dynamic models have been developed by researchers to simulate the acceleration signal of the bearing and enhance the understanding of bearing vibration response in case of having local defects. Nevertheless, recent studies have shown that measurement of local strain in the bearing can be more effective in fault diagnosis of bearings since local strain signal is less subjected to surrounding noise and non-fault interferences. However, previously developed dynamic models lack a deep study on simulating strain signals at different locations in a bearing. Therefore, this paper presents the result of a combined lumped parameter and finite element models (FEMs) for simulating strain signals of a cylindrical roller bearing in healthy and defective conditions. Contrary to previous combined dynamic models, more realistic modeling assumptions are adopted, such as applying contact pressure to the raceway rather than concentrated nodal forces. The contact pressures are obtained from the lumped parameter model and exported to the FEM for transient analysis. The accuracy of the model in generating stress distribution is validated by a static 2D finite element contact model and theoretical formulas. Dynamic responses of the bearing, such as strain changes at different locations, are investigated in the time and frequency domains to investigate the fault symptoms such as sharp impulses and amplitude modulation. Lastly, the effect of a transmission path on strain measurement and using residual strain signal for fault diagnosis are discussed. Overall, the proposed model is an effective method for strain simulation of healthy and defective roller 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".