Uncertainty Quantification in Gear Remaining Useful Life Prediction Through an Integrated Prognostics Method
Why this work is in the frame
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Bibliographic record
Abstract
Accurate health prognosis is critical for ensuring equipment reliability and reducing the overall life-cycle costs. The existing gear prognosis methods are primarily either model-based or data-driven. In this paper, an integrated prognostics method is developed for gear remaining life prediction, which utilizes both gear physical models and real-time condition monitoring data. The general prognosis framework for gears is proposed. The developed physical models include a gear finite element model for gear stress analysis, a gear dynamics model for dynamic load calculation, and a damage propagation model described using Paris' law. A gear mesh stiffness computation method is developed based on the gear system potential energy, which results in more realistic curved crack propagation paths. Material uncertainty and model uncertainty are considered to account for the differences among different specific units that affect the damage propagation path. A Bayesian method is used to fuse the collected condition monitoring data to update the distributions of the uncertainty factors for the current specific unit being monitored, and to achieve the updated remaining useful life prediction. An example is used to demonstrate the effectiveness of the proposed method.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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