Machine Learning Based Dynamic Failure Criteria for Reliability Analysis of Bearings
Why this work is in the frame
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Bibliographic record
Abstract
Accurate estimation of the reliability for different mechanical components plays an important role in the design and maintenance of mechanical systems. In this regard, a new method is proposed for increasing the accuracy of reliability prediction of bearings by introducing a new approach to determine dynamic failure criteria. To be specific, a Bayesian network classifier is applied to establish a machine learning approach for the determination of failure criteria at each time step with varying working and physical condition. The resulted failure criteria at each time are utilized together with a Kriging estimator to express an updated limit state function. Consequently, the second order reliability method is used for the calculation of time-varying reliability. Finally, the presented method is applied for reliability analysis of rolling element bearings and the resulted reliability curve for both accelerated and normal working conditions are presented. The outcome of this work can result in a pertinent approach for further calculation of the reliability of complex mechanical systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it