An Integrated Prognostics Method Under Time-Varying Operating Conditions
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Bibliographic record
Abstract
In this paper, we develop an integrated prognostics method considering a time-varying operating condition, which integrates physical gear models and sensor data. By taking advantage of stress analysis in finite element modeling (FEM), the degradation process governed by Paris' law can adjust itself immediately to respond to the changes of the operating condition. The capability to directly relate the load to the damage propagation is a key advantage of the proposed integrated prognostics approach over the existing data-driven methods for dealing with time-varying operating conditions. In the proposed method, uncertainties in material parameters are considered as sources responsible for randomness in the predicted failure life. The joint distribution of material parameters is updated as sensor data become available. The updated distribution better characterizes the material parameters, and reduces the uncertainty in life prediction for the specific individual unit under condition monitoring. The update process is realized via Bayesian inference. To reduce the computational effort, a polynomial chaos expansion (PCE) collocation method is applied in computing the likelihood function in the Bayesian inference and the predicted failure time distribution. Examples based on crack propagation in a spur gear tooth are given to demonstrate the effectiveness of the proposed method. In addition, the example also shows that the proposed approach is effective even when the current loading profile is different from the loading profile under which historical data were collected.
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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 | 0.000 |
| 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) | 0.000 | 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