Prognostics of Remaining Useful Life for Aviation Structures Considering Imperfect Repairs
Bibliographic record
Abstract
Maintenance plays an important role in fulfilling the goals ofthe Prognostics and Health Management (PHM) field. As ofnow, no publication has addressed the impact of imperfectrepair actions from the prognostics perspective. Imperfectrepairs introduce complexities, altering system degradationprocesses and increasing prediction uncertainties, thereby impactingthe accuracy of Remaining Useful Life (RUL) predictions.To fill this gap in the literature, the study proposes developinga robust prognostic model adaptable to post-repairoperations. The prognostic model that will be developed isstochastic since stochastic models have already proven theiradaptability to unseen test data. However, further developmentof such models is needed to deal with data on repairedsystems. In addition to that, the implementation of a BayesianExtension allows uncertainty interpretability to be consideredto account for the uncertainty coming from the repair actionitself but also from the different sources of uncertainties thathave not been studied in the field of prognostics.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".